Integrating Concepts in Biology

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Integrating Concepts in Biology
PowerPoint Slides for Chapter 8: Evolution of Organisms Section 8.1: What causes individual variation? by A. Malcolm Campbell, Laurie J. Heyer, and Chris Paradise

Relationship between height of parents and offspring
slope of one best-fit line Size of circles proportional to the number of comparisons Figure 8.1 Data showing the relationship between height of parents and offspring. The dashed line indicates a slope of one and the solid line indicates the best-fit line indicated by equation. Size of circles is proportional to the number of comparisons. Total sample size = 928 offspring and 205 sets of parents. Talking points: The size of the points indicates the number of data points at that point. Larger dots mean more pairs of parent midpoint and child fall at those coordinates. If the height of children was the same as the midpoint height of the parents, the best-fit line would be the same as the dashed line slope of 1. The slope from the best-fit regression is less than that, at 0.65, as indicated in the upper left of the graph. Figure 8.1

BME 8.1: How does linear regression work?
Midparent Child Midparent Squared Midparent times Child Coef on m Coef on b RHS 70.5 61.7 63390 68.5 928 65.5 64.5 m= 0.646 64 4096 3948.8 b= 23.942 67.5 62.2 4198.5 BME 8.1: How does linear regression work. Talking points: the next few slides help explain and work through equations in BMI 8.1.

BME 8.1: How does linear regression work?
Midparent Child Midparent Squared Midparent times Child Coef on m Coef on b RHS 70.5 61.7 63390 68.5 928 65.5 64.5 m= 0.646 64 4096 3948.8 b= 23.942 67.5 62.2 4198.5 slope BME 8.1: How does linear regression work. Talking points: Slope and intercept are shown in these boxes and are found from equation in BME 8.1. Next few slides show how equation works. y-intercept

BME 8.1: How does linear regression work?
Consider: rm+sb=t um+vb=w Solve for m and b: m=(sw-tv)/(su-rv) b=(tu-rw)/(su-rv) Coef on m Coef on b RHS 63390 928 m= 0.646 b= 23.942 BME 8.1: How does linear regression work. Talking points: Consider the equations in top left box. Solving for m and b allows you to find the slope and y-intercept. Next slide shows how terms in left equation match up with terms in table on right.

BME 8.1: How does linear regression work?
Consider: rm+sb=t um+vb=w Solve for m: m=(sw-tv)/(su-rv) Σ(xi2) * m + Σ(xi) * b = Σ(xi * yi) Σ(xi) * m + n*b = Σ(yi) BME 8.1: How does linear regression work. Talking points: This shows the actual equation for m, the slope, plus how the terms used in the Excel formula to find the slope.

BME 8.1: How does linear regression work?
Consider: rm+sb=t um+vb=w Solve for m: m=(sw-tv)/(su-rv) Σ(xi2) * m + Σ(xi) * b = Σ(xi * yi) Σ(xi) * m + n*b = Σ(yi) (E) Coef on m (F) Coef on b (G) RHS 63390 928 m= 0.646 b= 23.942 BME 8.1: How does linear regression work. Talking points: This shows the actual equation for m, the slope, plus how the terms used in the Excel formula to find the slope.

BME 8.1: How does linear regression work?
Consider: rm+sb=t um+vb=w Solve for m: m=(sw-tv)/(su-rv) Σ(xi2) * m + Σ(xi) * b = Σ(xi * yi) Σ(xi) * m + n*b = Σ(yi) (E) Coef on m (F) Coef on b (G) RHS 63390 928 m= 0.646 b= 23.942 Σ(xi2) Σ(xi) Excel formula for m: =((F2*G3)-(G2*F3))/((F2*E3)-(E2*F3)) =[(Σ(xi)*Σ(yi)) – (Σ(xi*yi)*(n)] / [(Σ(xi)*Σ(xi)) – (Σ(xi2)*(n))] = ((s * w) – (t * v)) / ((s * u) – (r * v)) BME 8.1: How does linear regression work. Talking points: This shows the actual equation for m, the slope, plus how the terms used in the Excel formula to find the slope.

BME 8.1: How does linear regression work?
Consider: rm+sb=t um+vb=w Solve for b: b=(tu-rw)/(su-rv) Σ(xi2) * m + Σ(xi) * b = Σ(xi * yi) Σ(xi) * m + n*b = Σ(yi) (E) Coef on m (F) Coef on b (G) RHS 63390 928 m= 0.646 b= 23.942 Σ(xi2) Σ(xi) BME 8.1: How does linear regression work. Talking points: This shows the actual equation for m, the slope, plus how the terms used in the Excel formula to find the slope. Excel formula for b: =((G2*E3)-(E2*G3))/((F2*E3)-(E2*F3)) =[((Σ(xi*yi)*Σ(xi)) – (Σ(xi2)*Σ(yi)] / [(Σ(xi)*Σ(xi)) – (Σ(xi2)*(n))]

Mean blood pressures for rats in two colonies
Figure 8.2 Mean blood pressures for rats in the two colonies, measured in millimeters of mercury. All high blood pressure rats were homozygous for the adducin genes (αY and βR). Low blood pressure rats were all homozygous for the αF gene only. Talking points: The scientists developed two colonies of rats by breeding rats that had low blood pressure with each other in one colony, and rats that had higher blood pressure with each other in the other colony. In each successive generation, they removed rats that had higher blood pressure from the low blood pressure colony. They did the same for low blood pressure rats in the high blood pressure colony. After 85 generations, the blood pressures were very similar among individuals within each colony, but very different between individuals in different colonies. Figure 8.2

Portions of the β and α adducin subunit DNA sequences and corresponding amino acid sequence
letters correspond to different amino acids Figure 8.3 Portions of the β adducin (a) and α adducin (b) subunit DNA sequences and corresponding amino acid sequence. Numbers below the amino acid sequence indicate the position along the protein, and the letters correspond to different amino acids. At position 529 and 316 of β and α subunits, respectively, there is a mutation. The top letter in each sequence corresponds to the allele associated with high blood pressure. Talking points: The different versions, or alleles, of each protein subunit differed by only one amino acid. For each subunit, the genetic code is shown for one section in the top row, and letters corresponding to amino acids in the bottom row. The point mutation in each subunit is represented by two letters stacked on top of one another. position along the protein Figure 8.3

Mean blood pressures for rats in two colonies
αY/αY ; βR/βR αF/αF ; β_/β_ Figure 8.2 Mean blood pressures for rats in the two colonies, measured in millimeters of mercury. All high blood pressure rats were homozygous for the adducin genes (αY and βR). Low blood pressure rats were all homozygous for the αF gene only. Talking points: The scientists developed two colonies of rats by breeding rats that had low blood pressure with each other in one colony, and rats that had higher blood pressure with each other in the other colony. In each successive generation, they removed rats that had higher blood pressure from the low blood pressure colony. They did the same for low blood pressure rats in the high blood pressure colony. After 85 generations, the blood pressures were very similar among individuals within each colony, but very different between individuals in different colonies. In addition, high BP rats were homozygous at each of two loci in each colony; low BP rats were homozygous at one locus only. Figure 8.2

Q and R refer to the amino acid at position 529 of β subunit
Systolic BPs of the 3 combinations of 2 versions of the β adducin gene in rats from the low BP colony Q and R refer to the amino acid at position 529 of β subunit Figure 8.4 Mean systolic blood pressures of the three combinations of two versions of the β adducin gene in rats from the low blood pressure colony. Q and R refer to the amino acid present at position 529 on the β protein subunit of adducin. Talking points: There were differences among the three genotypes in the low blood pressure rats. Figure 8.4

BP of high BP parental strain
BP of 9 combinations of two versions of the α and β adducin genes in rats after two generations of breeding low and high blood pressure rats together. BP of high BP parental strain BP of low BP parental strain Figure 8.5 Mean blood pressures of the nine combinations of two versions of the α and β adducin genes in rats after two generations of breeding low and high blood pressure rats together. Error bars = 1 SE. Mean blood pressure (horizontal solid lines) ± 1 standard error (stippled areas) of 10 rats of each parental strain (HBP = high and LBP = low blood pressure) are included for comparison. Talking points: While the scientists identified the two adducin genes as playing a role in high blood pressure, there are other genes involved. You can conclude this based on the data in Figure The two alleles of the two subunits of adducin interact when in different combinations to cause a wide variation in high blood pressure, as indicated by the mean blood pressures of rats in the second generation of crossbreeding. However, none of these rats has as low or high of a blood pressure as their grandparents, and that variation is caused by other genes also controlling blood pressure. After two generations of crossbreeding, the different alleles of these other genes, from the homozygous grandparents, had been reshuffled independently of the adducin genes, increasing the variation of responses. Figure 8.5

Variation of combinations of the two adducin genes
All parents are heterozygous a. Range of alleles contributed from male parent F / Q F / R Y / Q Y / R Range of alleles contributed from female parent FF / QQ FF / QR FY / QQ FY / QR FF / RR FY / RR YY / QQ YY / QR YY / RR b. Range of gene versions contributed from male parent Table 8.1 Variation of combinations of the two adducin genes. All parents in the matings below are heterozygous for both the adducin subunits and can contribute one of two alleles for each gene to their offspring. a. Variation when the genes are located on two different chromosomes, showing independent assortment. b. Variation when the genes are on the same chromosome, with no crossing over. Talking points: Independent assortment leads to recombination. Recombination leads to new combinations of genes in offspring that did not occur in the parents, by crossing-over and independent assortment. Applying the concept of independent assortment, Bianchi and his colleagues knew that the genes coding for the two subunits of adducin were located on different chromosomes, so they could predict that when two heterozygous individuals mated, offspring of those individuals would exhibit one of nine different combinations of the two genes. Table 8.1

Zinc and copper contamination and pH in soils surrounding a smelting operation in Pennsylvania
Species and site Zinc (ppm) Copper (ppm) Sandwort – near 7,500 15 Sandwort – medium 3,344 10 Sandwort – far 975 2.5 Honeysuckle – near 5,875 3 Honeysuckle – far 40 1 Table 8.2 Zinc and copper contamination (in parts per million (ppm)) and soil pH in soils surrounding a smelting operation in Pennsylvania. Sandwort plants were collected at three sites (near, medium, far) and honeysuckle plants were collected at two sites (near and far). Talking points: Students should be able to describe the variation in environmental factors as distance from the smelting operation increases. This is an example of a gradient or environmental cline. Some environmental factors, such as the concentrations of metals, are more variable than others, such as pH. Table 8.2

Stomata and hair densities of sandwort collected at two times and grown in controlled conditions
Figure 8.6 Stomata and hair densities of two plants collected at two times and grown in controlled conditions in a courtyard. Sandwort plants from the intermediate site were not grown in the courtyard. Talking points: Sandwort has some variation when plants from different sites are grown under controlled conditions. There appears to be some genetic component, more evident with stomata than hairs. What do the results of the common garden experiment show? Figure 8.6

Stomata and hair densities of honeysuckle collected at two times and grown in controlled conditions
Figure 8.6 Stomata and hair densities of two plants collected at two times and grown in controlled conditions in a courtyard. Sandwort plants from the intermediate site were not grown in the courtyard. Talking points: Honeysuckle still has variation when plants from different sites are grown under controlled conditions. This indicates that there is a genetic component to the differences, and the differences are not solely due to the environmental gradient. What do the results of the common garden experiment show? Figure 8.6

Responses of the acorn barnacle (Chthamalus anisopoma) to the snail predator (Acanthina).
bent and cone shell shapes Figure 8.7 Responses of the acorn barnacle (Chthamalus anisopoma) to the snail predator (Acanthina). a. Bent (top) and cone (bottom) shell shapes. b. Results of predator exclusion experiment. c. Survival of two types of barnacles in plots with and without the predator. Talking points: The bent variety is less common overall than the cone shape barnacles. Acanthina image not in textbook. Spine used to pry barnacles Figure 8.7

Responses of the acorn barnacle (Chthamalus anisopoma) to the snail predator (Acanthina).
results of predator exclusion experiment Figure 8.7 Responses of the acorn barnacle (Chthamalus anisopoma) to the snail predator (Acanthina). a. Bent (top) and cone (bottom) shell shapes. b. Results of predator exclusion experiment. c. Survival of two types of barnacles in plots with and without the predator. Talking points: A predator can cause developmental changes. The bent variety is less common overall than the cone shape and never developed in the absence of the predator, but a significant number of them developed in the presence of the predator. The increase in density of the cone variety was not as great in the presence of the snail than in the absence of the snail, and this phenotype was present in both the presence and absence of the snail. This increases variation among individuals in the population of barnacles across areas with and without predators. Even in the presence of the predator there were a large number of cone shaped barnacles, indicating that these individuals either did not receive the “predator cue” during development or they were genetically incapable of growing into the bent shape. Environmental factors can alter the physical appearance of animals, but only within the scope of their genetic potential. The bent shape may be rare in the absence of the predator because of a decrease in feeding efficiency, but the benefit of having this shell shape in the presence of the predator is that it increases the probability of survival. Figure 8.7

Responses of the acorn barnacle (Chthamalus anisopoma) to the snail predator (Acanthina).
without predator Survival of barnacles with predator Figure 8.7 Responses of the acorn barnacle (Chthamalus anisopoma) to the snail predator (Acanthina). a. Bent (top) and cone (bottom) shell shapes. b. Results of predator exclusion experiment. c. Survival of two types of barnacles in plots with and without the predator. Talking points: The bent shape may be rare in the absence of the predator because of a decrease in feeding efficiency, but the benefit of having this shell shape in the presence of the predator is that it increases the probability of survival. Figure 8.7

Integrating Concepts in Biology
PowerPoint Slides for Chapter 8: Evolution of Organisms Section 8.2: How does selection act on individuals with variable characteristics? by A. Malcolm Campbell, Laurie J. Heyer, and Chris Paradise

Poecilia reticulata http://www.erdingtonaquatics.com/guppies.html
Guppies, image not in text

Survival of guppies with different behavioral tendencies to inspect potential predators
Figure 8.8 Total survival (pooled for all ten trials) of guppies with different behavioral tendencies to inspect potential predators. Survival was measured at two time points during the experiment. Talking points: Dugatkin showed that bold individuals do not survive very long in the presence of a predator. Figure 8.8

Mean number of predator inspections by bright and drab male guppies
Is there an effect of brightness or predation? Figure 8.9 Mean number of predator inspections by bright and drab male guppies (per 30 minute observation time). Talking points: In the presence of female guppies, bright guppies are more likely than drab guppies to swim towards, and inspect, potential predators. Figure 8.9

Effects of presence of female and male color on predator inspection behavior
Predator inspections initiated by bright or drab males when females either present or absent. Relationship between boldness and brightness of males. Relationship between minimum distance of an approaching predator and brightness of males. Figure Effects of presence of female and male color on predator inspection behavior. a. Predator inspections initiated by bright or drab males when females are either present or absent (n = 24 pairs of males). b. Relationship between boldness (# of inspections per 30 minutes) and brightness of males. c. Relationship between minimum distance of an approaching predator and brightness of males. Talking points: Colorful males generally bolder, especially in the presence of females. Drab males spent less time inspecting predators because they spent more time near females when females were present. The bright guppies are also more likely to flee from predators before the predator gets too close. Figure 8.10

Preferences of female guppies in choice tests
Figure Preferences of female guppies in choice tests. Females were exposed to pairs of males, one bright and one drab, and boldness and timidity were simulated by the experimenters. Females could then choose with which male to initiate courtship display. Sample size = 20 for each mate choice comparison. Talking points: Females preferred to mate with bold males when a predator was nearby and bright males when predators were absent. Inspection behavior was not an issue when there was no predator nearby, but because of the correlation between brightness and boldness, females assess male boldness by examination of their color alone. Figure 8.11

BME 8.2: Do females really prefer bold males?
When a predator was present, 14 / 20 female guppies preferred the bright male to the drab male when the bright male was the bold one 16 / 20 preferred the drab male to the bright male when the drab male was the bold one. BME Do females really prefer bold males? This BME illustrates how you can determine statistical significance in preference tests. The p-values of the preference tests on the data in Figure 8.11 are based on the binomial distribution. Talking points: The counts show strong preferences, but how can you be sure these differences were not just due to chance? Anytime you are trying to show a difference between populations, you need to report the statistical significance, or p-value.

BME 8.2: Do females really prefer bold males?
p-value of preference test = probability of getting at least the observed number of guppies choosing one color and behavior combination. Here, the p-value = probability of at least 14 of 20 guppies making this choice. BME IQ 8.2a leads to estimate of this probability. In BME IQ 8.2e, you computed the probability of exactly 14 heads in 20 tosses of a fair coin =(1/2)20 x number of arrangements in which there are exactly 14 heads (20 choose 14). =38,760 x (1/2)20 = Probability of at least 14 heads in 20 tosses, repeat for situations of 15 – 20 heads, and sum. BME Do females really prefer bold males? This BME illustrates how you can determine statistical significance in preference tests. The p-values of the preference tests on the data in Figure 8.11 are based on the binomial distribution. Talking points: Students calculate the probability of at least 14 heads in 20 tosses of a fair coin.

Summary of guppy experiments
Bold males selected against in first experiment. In presence of females, bright more likely than drab guppies to swim towards, and inspect, potential predators. Bright guppies more likely to flee before predator gets too close. Females preferred to mate w/ bold males when predator was nearby and bright males when predator absent; appearance = boldness? Bold males inspections may signal to predator that it has been spotted. Bold individuals more aware? Bold, healthy males may contribute more advantageous genes. Phenotypes remain in population when providing advantage to possessor; phenotypes selected against reduce ability of individual to survive or reproduce.

Spatial pattern of flower color along two transects that cut through a ravine of desert snow (Linanthus parryae) Position of ravine dividing populations Figure Spatial pattern of flower color along a north (a) and south (b) transect that cut through a ravine, and estimates of frequencies for four allozymes along the north (c) and south (d) lines. The vertical bar near the center of each panel marks the position of the ravine that divides the populations. Talking points: Note that both transects show the same pattern – on one side of the ravine there is a predominance of white flowers, and on the other a predominance of blue flowers. That is suggestive of two populations adapted to two microhabitats, although short dispersal and chance could also explain the distribution of flower types. How does the frequency of the white allele change across the ravine? Figure 8.12

Estimates of frequencies for four allozymes along each transect
How does the spatial pattern in flower color compare to the allele frequencies? Figure Spatial pattern of flower color along a north (a) and south (b) transect that cut through a ravine, and estimates of frequencies for four allozymes along the north (c) and south (d) lines. The vertical bar near the center of each panel marks the position of the ravine that divides the populations. Talking points: The frequency of one allele (determining flower color) changed across the ravine, something that alleles of the other genes tested do not do. Schemske and Bierzychudek found that four other genes from plants sampled across the transects varied little across the two sides of the ravine. That suggests no selection for or against those allozymes, and that only flower color is selected for in the habitats studied. Figure 8.12

Estimates of frequencies for four allozymes along each transect
No spatial pattern evident in these allozymes – note the slope of each line Figure Spatial pattern of flower color along a north (a) and south (b) transect that cut through a ravine, and estimates of frequencies for four allozymes along the north (c) and south (d) lines. The vertical bar near the center of each panel marks the position of the ravine that divides the populations. Talking points: The frequency of one allele (determining flower color) changed across the ravine, something that alleles of the other genes tested do not do. Schemske and Bierzychudek found that four other genes from plants sampled across the transects varied little across the two sides of the ravine. That suggests no selection for or against those allozymes, and that only flower color is selected for in the habitats studied. Figure 8.12

Spatial pattern of flower color along two transects that cut through a ravine, and estimates of frequencies for four allozymes along each Position of ravine dividing populations Figure Spatial pattern of flower color along a north (a) and south (b) transect that cut through a ravine, and estimates of frequencies for four allozymes along the north (c) and south (d) lines. The vertical bar near the center of each panel marks the position of the ravine that divides the populations. Talking points: Note that both transects show the same pattern – on one side of the ravine there is a predominance of white flowers, and on the other a predominance of blue flowers. That is suggestive of two populations adapted to two microhabitats, although short dispersal and chance could also explain the distribution of flower types. Figure 8.12

Mean seed production (± 1 s. e
Mean seed production (± 1 s.e.) for blue- and white-flowered desert snow plants from transplant plots Do you detect evidence for natural selection? Figure Mean seed production (± 1 s.e.) in 1995 and 1996 for blue- and white-flowered desert snow plants from plots located on the primarily blue-flowered side of the ravine and the primarily white-flowered side of the ravine. NS = not significant. Talking points: There were differences in seed production, although not consistent from year to year. Seed production, and thus reproductive success, varies for the two flower types in the two sides of the ravine. In 1995, a year with greater precipitation, white flowered plants on the white flowered side of the ravine produced more seeds per plant than blue flowered plants. This is suggestive of natural selection. In 1996, a drier than average year, all plants produced far fewer seeds, but blue flowered plants produced more seeds per plant than white flowered plants on the blue flowered side of the ravine. The differences do not have to persist every year in order for natural selection to cause the patterns observed. blue side white side Figure 8.13

Variation in plant cover on two sides of a ravine where desert snow grows
Asterisks indicate that the cover for a species was statistically different on the two sides. What do the patterns suggest to you? Figure Environmental variation on two sides of a ravine where desert snow grows. a. Area covered for 10 plant species. Asterisks indicate that the cover for a species was statistically different on the two sides. b. Differences in soil composition along a transect that crossed the ravine. * = 0.05, ** = 0.01, *** = 0.001, **** = probability of the observed differences if the null hypothesis of no difference is true. Talking points: The large differences found among the various other plant species tested is highly indicative of different microhabitats. The other species of plants present on either side of the ravine, could be the source of selection for flower color on the two sides of the ravine, although Schemske and Bierzychudek did not test individual factors in the soil or in the plant community. Figure 8.14

Differences in soil composition along a transect that crossed the ravine
Asterisks indicate that the variable was statistically different on the two sides. What do the patterns suggest to you? Figure Environmental variation on two sides of a ravine where desert snow grows. a. Area covered for 10 plant species. Asterisks indicate that the cover for a species was statistically different on the two sides. b. Differences in soil composition along a transect that crossed the ravine. * = 0.05, ** = 0.01, *** = 0.001, **** = probability of the observed differences if the null hypothesis of no difference is true. Talking points: How the soil differences came to be so great over such a small spatial scale is unknown, although they could be due to some past flooding event. The soil environment or some other unknown, unmeasured factor could have then given rise to variation in plant community composition. Figure 8.14

Integrating Concepts in Biology
Chapter 8: Evolution of Organisms Section 8.3: Can you observe descent with modification?

Evolutionary tree of major groups of plant species.
To what group of species are flowering plants most closely related? The species that existed at branch point “b” was the common ancestor of what groups of species? Figure Evolutionary tree of major groups of plant species. The base of the tree trunk represents the common ancestor of plants. Flowering plants include orchids. See text for explanation of a, b, and c. Talking points: An evolutionary tree can be developed to show the hypothesized relationships among these groups of organisms. The species that existed at branch point “b” was the common ancestor of ferns, the pines, spruce, and fir (collectively called conifers), and the flowering plants. All species of flowering plants had a single common ancestor, which existed on that branch just after point “c” where the conifers split off from the flowering plants. Figure 8.15

Morphology and pollen packet placement of orchids
The pollen packet lies at the end of the erect anther. Figure 8.16 Morphology and pollen packet placement of modern orchids thought to be related to M. caribea (a) and hypothetical reconstruction of floral morphology of M. caribea (b). Orchid flowers often have a lip (lp) upon which pollinators land. The pollen packet (pl) lies at the end of the erect anther (an). Talking points: Orchid pollen found attached to the back of a fossil bee. Between its wings were two packets of pollen from a single flower, with each packet containing many pollen grains. The phenotype of the fossilized pollen packets is found only in one subfamily of the orchid family. Based on characteristics of the pollen and how it was attached to the back of the bee, the scientists were able to hypothesize what the flowers of this species looked like. In living orchids thought to be related to M. caribea the placement of pollen packets results in attachment on the pollinator's mouthparts. Attachment of the pollen to the dorsal surface of a worker bee is only possible with the arrangement shown in b, where the insect crawls into the flower and the pollen packet is stuck to the bee as it retreats back out. Orchid flowers often have a lip Figure 8.16

Meliorchis caribea. This orchid
pollinarium, carried by a worker stingless bee, is preserved in amber and represents the first definitive fossil record for the family Orchidaceae. (scale bar, 1,000 mm). Figures not in text; the one on the left is from the original research and shows the fossil on which the evolutionary trees are made. The figure on the right shows a similar, related and extant bee and orchid.

Phenotypic characters for several orchid genera.
Specimen a b c d e f g h i j k l m n Altensteinia 1 2 Gomphichis Goodyera Microchilus Meliorchis Table 8.3 Phenotypic characters for several orchids listed by genus. Only a subset of the species and phenotypes used by Pierce and her colleagues is shown here. Meliorchis is the fossilized orchid. Each phenotype is denoted by a letter. Talking points: The scientists compared the ancient species to living species and constructed a table of phenotypes, with the particular shape or structure of each phenotypic character. Phenotypes of some orchids are not similar to the fossil species. Table 8.3

Phenotypic characters for several orchid genera.
Specimen a b c d e f g h i j k l m n Altensteinia 1 2 Gomphichis Goodyera Microchilus Meliorchis Table 8.3 Phenotypic characters for several orchids listed by genus. Only a subset of the species and phenotypes used by Pierce and her colleagues is shown here. Meliorchis is the fossilized orchid. Each phenotype is denoted by a letter. Talking points: Traits of living orchids are compared to the fossil orchid. The scientists compared the ancient species to living species and constructed a table of phenotypes, with the particular shape or structure of each phenotypic character. Phenotypes of some orchids are not similar to the fossil species. Living orchids Table 8.3

Phenotypic characters for several orchid genera.
Specimen a b c d e f g h i j k l m n Altensteinia 1 2 Gomphichis Goodyera Microchilus Meliorchis Table 8.3 Phenotypic characters for several orchids listed by genus. Only a subset of the species and phenotypes used by Pierce and her colleagues is shown here. Meliorchis is the fossilized orchid. Each phenotype is denoted by a letter. Talking points: The scientists compared the ancient, fossilized species to living species and constructed a table of phenotypes, with the particular shape or structure of each phenotypic character. Phenotypes of some orchids are not similar to the fossil species. The fossilized orchid Table 8.3

Phenotypic characters for several orchid genera.
Phenotypes denoted by letters Specimen a b c d e f g h i j k l m n Altensteinia 1 2 Gomphichis Goodyera Microchilus Meliorchis Table 8.3 Phenotypic characters for several orchids listed by genus. Only a subset of the species and phenotypes used by Pierce and her colleagues is shown here. Meliorchis is the fossilized orchid. Each phenotype is denoted by a letter. Talking points: The scientists compared the ancient species to living species and constructed a table of phenotypes, with the particular shape or structure of each phenotypic character. Phenotypes of some orchids are not similar to the fossil species. Each phenotype is in a column, and you can compare the 5 genera by going down the columns. For instance, the trait “d” is the same for all 5 genera, which is not helpful in resolving evolutionary relationships within the group, except that it tells us they all have that phenotype in common. Table 8.3

Phenotypic characters for several orchid genera.
Particular form of a phenotype coded as a number Specimen a b c d e f g h i j k l m n Altensteinia 1 2 Gomphichis Goodyera Microchilus Meliorchis Table 8.3 Phenotypic characters for several orchids listed by genus. Only a subset of the species and phenotypes used by Pierce and her colleagues is shown here. Meliorchis is the fossilized orchid. Each phenotype is denoted by a letter. Talking points: The scientists compared the ancient species to living species and constructed a table of phenotypes, with the particular shape or structure of each phenotypic character. Phenotypes of some orchids are not similar to the fossil species. Each phenotype is in a column, and you can compare the 5 genera by going down the columns. For instance, the trait “d” is the same for all 5 genera, which is not helpful in resolving evolutionary relationships within the group, except that it tells us they all have that phenotype in common. Phenotype “a” is the same for all genera except Gomphichis. Table 8.3

Figure BME 8.3.1 Three rooted trees (b, c, d) consistent with the same unrooted tree (a).
Figure BME Three rooted trees (b, c, d) that are consistent with the same unrooted tree (a). Tree b is rooted at point B, species 1 (blue), tree c is rooted at point C and tree d is rooted at point D, species 3 (yellow). Talking points: The goal of this BME is to construct a tree that best represents the evolutionary relationships among these five species based on the characteristic data in Table 8.3. This BME helps students to understand how evolutionary trees are constructed, how they are depicted, and how to compare different trees. Seven rooted trees can be constructed that correspond to the unrooted tree. Three of them are shown in b-d. There are 15 different ways of drawing the unrooted tree. The spreadsheet parsimony.xlsx lists them and helps you determine the number of mutations for each tree and phenotype combination. The calculations in the spreadsheet are only valid for binary characteristics (values of either 0 or 1). Students should be able to draw the other 4 trees – can do it as an in-class exercise.

Figure BME 8.3.1 Three rooted trees (b, c, d) consistent with the same unrooted tree (a).
Figure BME Three rooted trees (b, c, d) that are consistent with the same unrooted tree (a). Tree b is rooted at point B, species 1 (blue), tree c is rooted at point C and tree d is rooted at point D, species 3 (yellow). Talking points: The goal of this BME is to construct a tree that best represents the evolutionary relationships among these five species based on the characteristic data in Table 8.3. This BME helps students to understand how evolutionary trees are constructed, how they are depicted, and how to compare different trees. Seven rooted trees can be constructed that correspond to the unrooted tree. Three of them are shown in b-d.

Figure BME 8.3.1 Three rooted trees (b, c, d) consistent with the same unrooted tree (a).
Tree b is rooted at point B, species 1 (blue) Figure BME Three rooted trees (b, c, d) that are consistent with the same unrooted tree (a). Tree b is rooted at point B, species 1 (blue), tree c is rooted at point C and tree d is rooted at point D, species 3 (yellow). Talking points: The goal of this BME is to construct a tree that best represents the evolutionary relationships among these five species based on the characteristic data in Table 8.3. This BME helps students to understand how evolutionary trees are constructed, how they are depicted, and how to compare different trees. Seven rooted trees can be constructed that correspond to the unrooted tree. Three of them are shown in b-d. When a tree is rooted at point B, the blue species branches off first. All the other species are more derived than the more ancestral blue species.

Figure BME 8.3.1 Three rooted trees (b, c, d) consistent with the same unrooted tree (a).
Tree c is rooted at point C Figure BME Three rooted trees (b, c, d) that are consistent with the same unrooted tree (a). Tree b is rooted at point B, species 1 (blue), tree c is rooted at point C and tree d is rooted at point D, species 3 (yellow). Talking points: The goal of this BME is to construct a tree that best represents the evolutionary relationships among these five species based on the characteristic data in Table 8.3. This BME helps students to understand how evolutionary trees are constructed, how they are depicted, and how to compare different trees. Seven rooted trees can be constructed that correspond to the unrooted tree. Three of them are shown in b-d. When a tree is rooted at point C (see the arrow), the blue and purple species branch off first. All the other species are more derived than the more ancestral blue and purple species.

Figure BME 8.3.1 Three rooted trees (b, c, d) consistent with the same unrooted tree (a).
Tree d is rooted at point D, species 3 (yellow) Figure BME Three rooted trees (b, c, d) that are consistent with the same unrooted tree (a). Tree b is rooted at point B, species 1 (blue), tree c is rooted at point C and tree d is rooted at point D, species 3 (yellow). Talking points: The goal of this BME is to construct a tree that best represents the evolutionary relationships among these five species based on the characteristic data in Table 8.3. This BME helps students to understand how evolutionary trees are constructed, how they are depicted, and how to compare different trees. Seven rooted trees can be constructed that correspond to the unrooted tree. Three of them are shown in b-d. When a tree is rooted at point D (see the arrow), the yellow species branches off first (small error in that it shows the green species as most ancestral – that should be yellow). All the other species are more derived than the more ancestral yellow species.

BME 8.3 Possible unrooted trees for 5 species, with species 3 in middle position
1 4 3 2 5 1 2 3 4 5 1 5 3 2 4 BME Possible unrooted trees. There are three unrooted trees with a particular species in middle position. These three show the possibilities with species 3 in the middle position. Talking points: The goal of this BME is to construct a tree that best represents the evolutionary relationships among these five species based on the characteristic data in Table 8.3. This BME helps students to understand how evolutionary trees are constructed, how they are depicted, and how to compare different trees. Fifteen possible unrooted trees are possible.

BME 8.3 Best unrooted trees for 5 species
1 2 3 4 5 1 2 4 3 5 Required single mutation for b, k, and l occurs here 1 2 5 3 4 BME Best unrooted trees. Talking points: In each case, from the excel spreadsheet, trees 1, 10 and 13 require only one mutation. These are the three trees that place species 1 and 2 adjacent to each other in the tree, so the only required mutation is at the branch point leading to the two 0’s (or, equivalently, the branch point leading to the three 1’s). Among the three trees that are optimal for characteristics b, k and l, only tree 1 is also optimal for characteristic j. Therefore, tree 1 is the most parsimonious overall for explaining the inheritance of characteristics a through l.

Phenotypic characters for several orchid genera.
Specimen a b c d e f g h i j k l m n Altensteinia 1 2 Gomphichis Goodyera Microchilus Meliorchis Table 8.3 Phenotypic characters for several orchids listed by genus. Only a subset of the species and phenotypes used by Pierce and her colleagues is shown here. Meliorchis is the fossilized orchid. Each phenotype is denoted by a letter. Talking points: The scientists compared the ancient species to living species and constructed a table of phenotypes, with the particular shape or structure of each phenotypic character. Phenotypes of some orchids are not similar to the fossil species. Table 8.3

BME 8.3 Best unrooted trees for 5 species
1 2 3 4 5 Only tree #1 is optimal for j 1 2 4 3 5 1 2 5 3 4 BME Best unrooted trees. Talking points: In each case, from the excel spreadsheet, trees 1, 10 and 13 require only one mutation. These are the three trees that place species 1 and 2 adjacent to each other in the tree, so the only required mutation is at the branch point leading to the two 0’s (or, equivalently, the branch point leading to the three 1’s). Among the three trees that are optimal for characteristics b, k and l, only tree 1 is also optimal for characteristic j. Therefore, tree 1 is the most parsimonious overall for explaining the inheritance of characteristics a through l.

Phenotypic characters for several orchid genera.
Specimen a b c d e f g h i j k l m n Altensteinia 1 2 Gomphichis Goodyera Microchilus Meliorchis Table 8.3 Phenotypic characters for several orchids listed by genus. Only a subset of the species and phenotypes used by Pierce and her colleagues is shown here. Meliorchis is the fossilized orchid. Each phenotype is denoted by a letter. Talking points: The scientists compared the ancient species to living species and constructed a table of phenotypes, with the particular shape or structure of each phenotypic character. Phenotypes of some orchids are not similar to the fossil species. Table 8.3

Evolutionary tree of the orchid family
End of Cretaceous, 65 MYA Arrow heads indicate estimated ages of small subfamilies, A, B, and C Estimate of earliest existence of orchids, range can be determined from two scales, top and bottom Figure Evolutionary tree of the orchid family, showing the five subfamilies of orchids and based on DNA sequences and a timeline estimated from a fossil orchid. The size of each shaded area is proportional to the number of genera in each subfamily. Arrow heads indicate estimated ages of small subfamilies. Talking points: The scientists knew the fossilized orchid existed million years ago, and this allowed them to construct a timeline. The amber containing the fossil was embedded in rock estimated by geologists to be between 15 and 20 million years old. Orchid DNA sequences for 55 species of orchids distributed among all five orchid subfamilies were compared, and the similarities and differences in the sequences were used to construct an evolutionary tree. Rate of change of orchids in the subfamily to which Meliorchis belonged was estimated, based on the relationships in Table 8.3. This allowed them to create two timelines, based on the range of ages of the fossil. Species that are not very similar split a long time ago and similar species split more recently. The origin of orchids themselves was estimated to be million years ago. Figure 8.17

Evolutionary tree of the orchid family
Oldest age estimated from a fossil orchid, 20 MYA Fossil orchid, M. caribea, showing range of estimated fossil dates Figure Evolutionary tree of the orchid family, showing the five subfamilies of orchids and based on DNA sequences and a timeline estimated from a fossil orchid. The size of each shaded area is proportional to the number of genera in each subfamily. Arrow heads indicate estimated ages of small subfamilies. Talking points: The scientists knew the fossilized orchid existed million years ago, and this allowed them to construct a timeline. The amber containing the fossil was embedded in rock estimated by geologists to be between 15 and 20 million years old. Orchid DNA sequences for 55 species of orchids distributed among all five orchid subfamilies were compared, and the similarities and differences in the sequences were used to construct an evolutionary tree. Youngest age estimated from a fossil orchid, 15 MYA Figure 8.17

Evolutionary tree of the orchid family
Size of shaded area of a subfamily proportional to number of species in subfamily. E is most diverse Figure Evolutionary tree of the orchid family, showing the five subfamilies of orchids and based on DNA sequences and a timeline estimated from a fossil orchid. The size of each shaded area is proportional to the number of genera in each subfamily. Arrow heads indicate estimated ages of small subfamilies. Talking points: The scientists knew the fossilized orchid existed million years ago, and this allowed them to construct a timeline. The amber containing the fossil was embedded in rock estimated by geologists to be between 15 and 20 million years old. Here the size of the shaded area indicates the species richness of the subfamily. Subfamilies D and E are the most species-rich. Orchid DNA sequences for 55 species of orchids distributed among all five orchid subfamilies were compared, and the similarities and differences in the sequences were used to construct an evolutionary tree. Species that are not very similar split a long time ago and similar species split more recently. The origin of orchids themselves was estimated to be million years ago. Small black scale bar represents thickness of 50 genera of orchids Figure 8.17

Microhabitats of the forest canopy and the distribution of orchid species (open bars) and numbers of individuals (black bars) in the microhabitats. Figure Microhabitats of the forest canopy, pictured as a representative tree on left. The graph shows the frequency distribution of species (open bars) and numbers of individuals (black bars) in the microhabitats. Talking points: Living high enough to gather a lot of light but gaining protection through a strong attachment to branches or trunks appears to be of benefit to individuals and their descendants. Once it evolved, this adaptation led to many species of orchids all with the epiphytic lifestyle. Barbara Gravendeel and her colleagues tested the hypotheses that living above ground promotes species diversity and that having a smaller number of specialized pollinators promotes species diversity. Figure 8.18

Frequency distributions of plants living on trees (maroon) and on the ground (gold)
Orchids Non-orchid plants Figure Frequency distributions of orchids (a) and non-orchid plants (b) living on trees above ground (maroon bars) and terrestrially, on the ground (gold bars). The x-axis shows the range of species in a genus, and the y-axis represents the number of genera having that range of species. Talking points: Each orchid genus in the database contained at least one orchid species and lived either in trees or on ground. Non-orchid genera were also selected. The scientists determined how many genera had a certain range of species. For instance, there are 33 genera of tree-dwelling orchids that have between 2 and 5 species each. There are higher frequencies of non-orchid genera with only one species than orchid genera. More genera in both groups living on the ground than in trees. Figure 8.19

Mean number of pollinators / species for orchids in five subfamilies
Subfamilies same as in 8.17, D and E most diverse (# of species above bar) Figure Mean number of pollinators per species for orchids belonging to each of five subfamilies. Subfamilies are denoted by same lettering as in Figure Sample sizes for each subfamily were as follows: A = 2, B = 8, C = 4, D = 184, and E = Error bars represent 1 standard error. The number of species in a subfamily is shown above each bar. Talking points: Pollinator specialization does not seem to be related to high species diversity. The subfamily with the least number of species (subfamily A) has just over one pollinator per species, on average. The subfamily with the largest number of species (E) has over 3 pollinators per species. This led Gravendeel and her colleagues to reject the pollinator hypothesis, even though subfamily E, with the most species, has more specialized pollinators than subfamily D, with the second most species. Figure 8.20

Evolutionary relationships among birds, bats, and insects
Simplified cartoon of evolutionary relationships. Real tree is much more complex. Figure Evolutionary relationships among birds, bats, and insects. The evolutionary tree for all animals is much more complex. Talking points: The evolution of wings capable of flight appears to have occurred at least three different times in evolutionary history. We can infer this from the fossil record, anatomical studies, and our understanding of the evolutionary relationships among birds, bats, and insects, the three groups of animals capable of true flight (not just gliding). Figure 8.21

Evolution of wings (W) occurred twice
Evolutionary tree showing relationship among several vertebrates Figure Evolutionary tree of several vertebrates. The evolution of fur and mammary glands (F and M) separated mammals from other animals. By the time that happened, birds had already begun to evolve along their own lineage. Wings (W) evolved later in both groups. Talking points: Despite the close relationship between bats and birds, they still have distinct evolutionary histories. This figure shows four representative vertebrates. The evolution of fur and mammary glands appeared in mammals prior to wings evolving in bats. Birds evolved along a different evolutionary path. Evolution of wings (W) occurred twice Figure 8.22

Skeleton from living species
The fossil bat Icaronycteris index and skeleton from a living species Bones trapped in rock Skeleton from living species Figure The fossil bat Icaronycteris index. a. The bones are trapped in rock. b. Bat skeleton from a species that is living today. The bones of the elongated “fingers” are indicated by red arrows. Talking points: The oldest known fossil bat is estimated to be about 50 million years old. The bat species ancestral to the many species of living bats evolved from non-flying mammals, and the transition from non-winged to winged is hypothesized to be in the fossil record. Note the similarities in the skeletal features, especially the elongated digits, between the ancestral and living bat. Figure 8.23

Elongated fifth digit (metacarpal bone) of bats compared to a combined index of body size for living and fossil bats Fossil bats Living bats Figure The elongated fifth digit (metacarpal bone) of bats compared to a combined index of body size for living (blue circles) and fossil (red crosses) bats. The digit length is logarithm-transformed. Talking points: Inspection of the skeletons of fossil bats and living bats, along with the analysis of digit length vs. body size indicate that fossil bats look very much like modern bats. Niswander and her colleagues concluded that a bat that lived 50 MYA had fully formed wings and was capable of flight. Figure 8.24

Two hypotheses for selection of long digits
Gliding hypothesis bat- or squirrel-size creature that lived in Mongolia about 125 million years ago What are they? Jumping hypothesis Hypotheses from the text, but the image is not in the text. In neither case did they have muscles or behavior necessary for true flight

Percentage of growing 3rd -5th forelimb digits composed of proliferating and differentiating / elongating cells in mice and bats Figure The percentage of growing 3rd -5th forelimb digits composed of proliferating (green) and differentiating/elongating (blue) cells in mice (triangles) and bats (circles) at numbered developmental stages. Stages are numbered so that animals in the same stage can be compared. Talking points: The major difference in the growing portion of the bat digit cartilage is in the next to last step of cartilage cell maturation, and that this difference only appeared in the 3rd, 4th, and 5th digits of the forelimb, corresponding to our own middle finger, ring finger, and pinky. This is shown for the 5th digit as a plot of the percentage of cells in each step during developmental stages of the mouse and bat. As the percentage of cells in differentiation/elongation increased, so did the absolute length of the digits. Developmental stages with same number are equivalent. Figure 8.25

Percentage of cells in elongation and differentiation zone of metacarpals vs. length of the bat 5th metacarpal as development proceeds. 22 Developmental stages 21 20 Figure Percentage of cells in the elongation and differentiation zone of the 5th metacarpal and length of the 5th metacarpal as development proceeds. Talking points: As percentage of cells in differentiation /elongation increased, so did absolute length of the digits. 18 19 Figure 8.26

Bone Morphogenetic Protein 2
Experiment Result Expression levels of BMP2 31% higher in bat forelimb metacarpals than mice Bat forelimb cell cultures with BMP2+ 239 μm longer than control metacarpals Bat forelimb cell cultures with BMP2 inhibitor 183 μm shorter than control metacarpals

Integrating Concepts in Biology
Chapter 8: Evolution of Organisms Section 8.4 Can you observe evolution in your lifetime? by A. Malcolm Campbell, Laurie J. Heyer, and Chris Paradise

Incidence of malaria in Italy after a DDT spray campaign began
DDT and mosquitoes DDT Incidence of malaria in Italy after a DDT spray campaign began Anopheles gambiae female taking a blood meal. Figure DDT and mosquitoes. a. DDT. Each node is a carbon atom. The hexagons represent a ring of carbons with alternating single and double bonds. Hydrogen atoms are bonded to carbons without a chlorine (Cl). b. Incidence of malaria in Italy after a DDT spray campaign began. c. Anopheles gambiae female taking a blood meal. Talking points: DDT chemical structure is shown. DDT and mosquitoes have a long history. Early successes in defeating malaria by using DDT to kill mosquitoes ultimately failed in many countries. Figure 8.27

Relationship between incidence of malaria and DDT use in India, 1969-77
What do you conclude from what occurred in India? Figure Relationship between incidence of malaria (number of cases) and DDT use in India from Talking points: In any region where DDT was initially sprayed the reduction in mosquitoes and malaria cases followed the pattern observed in Italy. In India and other areas, mosquitoes evolved resistance to DDT and became unaffected by the pesticide. Their populations resurged and often the malaria returned with them. Figure 8.28

Malaria Transmission MAlaria Risk in Africa
Map of malaria risk in Africa, not in text; supplemental

Mortality of two populations of Anopheles gambiae when exposed to a series of DDT concentrations
Mortality rate Gambian population Tanzanian population 50% 0.017 0.339 90% 0.040 1.160 Table 8.4 Concentration of DDT (mg/L) that cause 50 and 90% mortality of two populations of Anopheles gambiae. Data obtained from Prapanthadara et al Figure 2. Talking points: Much lower concentrations of DDT were needed to kill off the Gambian mosquito population than the Tanzanian mosquito population. 50% of the Gambian population died when exposed to mg DDT / L and 90% died when exposed to mg DDT / L. Fifty percent of the Tanzanian population died when exposed to mg DDT / L and 90% died when exposed to mg DDT / L. The mosquitoes were offspring of individuals collected in the field, so there was no prior exposure, and it was concluded that the Tanzanian population had evolved resistance. Are Anopheles gambiae from Gambia more susceptible to DDT than those from Tanzania? Table 8.4

Results from extraction of GST from two populations of Anopheles gambiae
Mass of each variant of GST What is the significance of different amounts and activities of enzyme? Figure Results from extraction of GST from two populations of Anopheles gambiae, one from Gambia and one from Tanzania. a. Mass of each variant of GST. b. Ability of each variant to break down DDT. Activity for variants IVa, IVb, and IVc were not determined for the Gambia population due to low levels of the enzyme. Talking points: In the Tanzanian population there was a greater amount of almost every GST enzyme, and some of those enzymes had much higher activity in the resistant than the susceptible population. Exposure to toxic chemicals can lead to increased expression of genes, as is evident for several variants, but the higher activity for the same variant in the Tanzanian population than the Gambian population indicates a mutation changed the allele to one that detoxifies DDT faster. Figure 8.29

Time in minutes to 50 and 90% mortality of A
Time in minutes to 50 and 90% mortality of A. gambiae when exposed to permethrin and DDT Pesticide / Mortality rate Permethrin-exposed Kenyan pop. Permethrin-exposed Kenyan pop. w/ PBO DDT-exposed Tanzanian pop. Laboratory control pop. Permethrin 50% 32 25 nd 17 90% 98 79 36 DDT 57 145 <15 115 >250 41 Table 8.5 Time in minutes to 50 and 90% mortality of A. gambiae populations when exposed to a 0.5% solution of permethrin or a 4% DDT solution. pop = population; nd = not determined. Talking points: Permethrin acts in a manner similar to DDT, targeting the sodium channel in nerves, but is detoxified in insects by a different kind of enzyme. One A. gambiae population from Kenya had been exposed to bed nets impregnated with permethrin, another was the DDT-resistant population from Tanzania, and a third population was a susceptible strain that had been maintained in the laboratory through several generations. PBO inhibits the enzyme known to detoxify permethrin. These mosquitoes had two mechanisms of resistance. There was evidence that PBO inhibition led to an increased rate of mortality, which suggests the detoxification enzyme was being inhibited and would normally detoxify permethrin. Permethrin-resistant population somewhat resistant to DDT Table 8.5

When is there too much of a good thing? When chemicals are overused
ELSI Integrating Questions How do the proper and improper uses of pesticides and antibiotics inhibit or facilitate the evolution of resistance? What are the costs and benefits to society and ecological systems of pesticide use? What are the ethical and social considerations to be made when choosing whether to apply a pesticide or prescribe an antibiotic? ELSI 8.1