biology homework ASAP ( DUE IN 12 HOURS)

19.1 How does selection act on individuals with variable characteristics?

· Context: Natural selection is a mechanism of evolution that accounts for adaptation.

· Major Themes: Life continues to evolve within a changing environment and natural selection is a mechanism of evolution that accounts for adaptation.

· Bottom Line: Natural selection acts on variation among individuals, leading to varying success and advantages of some relative to others.

Biology Learning Objectives · Explain how the 5 tenets of natural selection influence individual organisms. · Evaluate how the processes by which variation is generated in organisms affects natural selection. · Explain how costs and benefits of performing different behaviors are assessed and how they relate to natural selection.

Variation of phenotypes among individuals in a population was an important concept to Darwin’s description of natural selection. The “preservation of favourable individual differences and variations, and the destruction of those which are injurious…” means that within the range of variation of phenotypes, some will be maintained and some will be eliminated. Individuals vary in their expression of phenotypes in a population, and you explored in Section 16.1 how both genes and the environment influence the variation of those phenotypes. Variation in alleles leads to variation in proteins, which can lead to variation in phenotypes like blood pressure, as you discovered, but also in anatomical and behavioral characteristics.

Selection can act on behaviors

Lee Dugatkin studied variation in the behavior of guppies ( Poecilia reticulata ) when exposed to a predator. Some guppies, but not all, perform an inspection behavior, where the individual leaves its school and swims slowly toward a potential predator. Dugatkin wanted to determine the costs and benefits of such behavior. Dugatkin collected 60 male guppies from a river in Trinidad, West Indies. The area of the river where guppies were collected was known to harbor several different species of fish that preyed on guppies.

Poecilia reticulata video.mp4 <div class="player-unavailable"><h1 class="message">An error occurred.</h1><div class="submessage"><a href="http://www.youtube.com/watch?v=x5uEFC5DgHM" target="_blank">Try watching this video on www.youtube.com</a>, or enable JavaScript if it is disabled in your browser.</div></div>	Guppy (Poecilia reticulata) <div class="player-unavailable"><h1 class="message">An error occurred.</h1><div class="submessage"><a href="http://www.youtube.com/watch?v=uVJ56wWhOSo" target="_blank">Try watching this video on www.youtube.com</a>, or enable JavaScript if it is disabled in your browser.</div></div>
Poecilia reticulata in the wild.	Poecilia reticulata in an aquarium - note the variation among the individuals.
Figure 19.1 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. From Godin and Dugatkin, 1996, Figure 1, © 1992, Oxford University Press.

Dugatkin first assessed each of the 60 guppies for their tendency to approach a predator fish. The guppies could see the predator in an adjacent aquarium, but the predator could not eat the guppy. Dugatkin measured how close the guppy came to the predator-containing aquarium. Based on how close each guppy was to the predator aquarium, Dugatkin classified it as being bold, ordinary, or timid. The bold guppies had high tendency to inspect and move close to the predator, the timid guppies had a low tendency to inspect and typically hid behind a plastic plant, and the ordinary fish exhibited an intermediate level of boldness.

Dugatkin then ran trials where groups of guppies were placed in one of ten aquaria with a predator (Figure 19.1). The groups were made of six fish: two bold, two ordinary, and two timid guppies. Guppies have individual variation in their color patterns, which Dugatkin used to tell individuals apart. After 36 and 60 hours, he collected all the fish and noted how many and which ones had survived to that point.

  

Bold individuals do not survive very long in the presence of a predator. This is an example of how natural selection works: In the context of the experimental conditions, the timid behavior is favored, whereas the bold behavior is not. Some fish exhibiting each type of behavior were consumed by the predator. The bold fish moved closer than timid fish, leading to their faster demise. Over time, predation acts as a selective agent, selecting against bold individuals.  Natural selection, through the agent of predation, should eliminate the bold behavior from the population. But the boldness behavior is still found in the population, because many of the guppies that Dugatkin collected from the wild were bold. The phenotype has not been eliminated from natural populations of guppies. To further explore the consequences of behavioral variability, Dugatkin, along with Jean-Guy Godin ran another series of experiments in which guppies were examined for their variability in behavior and another characteristic, brightness of color.

Their first experiment tested the hypothesis that brightly colored males were bolder than drab males (Figure 19.2).

Figure 19.2 Mean number of predator inspections by bright and drab male guppies (per 30 minute observation time). Data from Godin and Dugatkin, 1996, Figure1, © (1996) National Academy of Sciences, U.S.A

They used an aquarium with two compartments separated by a clear partition. In one compartment they placed a predator fish or nothing, and in the other they placed two male and two female guppies. One male was brightly colored, and the other was drab. After a 2 hour acclimation period, the scientists removed an opaque barrier between the compartments so that the guppies and the predator could see into the other compartment, but the predator was still prevented from preying on guppies because of a second clear partition. The scientists recorded the number of times in 30 minutes that each male guppy approached the compartment for many pairs of males.

In their next experiment, the scientists used a similar setup, but they varied the presence of females and used a model of a predator instead of a live predator (Figure 19.3). They suspended the model along a movable track so that they could remotely move the predator toward the guppies. The scientists created a color index related to the average size and brightness of color patches on a male. In the first set of trials, paired bright and drab males were placed in one compartment with or without a female. The scientists measured the number of approaches each male made toward the model predator (Figure 19.3A).

Figure 19.3 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 (number of inspections per 30 minutes) and brightness of males. C, Relationship between minimum distance of an approaching predator and brightness of males. Data from Godin and Dugatkin, 1996, Figures 2 & 3, © (1996) National Academy of Sciences, U.S.A

The scientists determined the relationship between the number of inspections that an individual made and the quantitative color index score of that male (Figure 19.3B). The next trial included a lone male in one compartment and the model predator in the other compartment. Dugatkin and Godin remotely moved the model from the far end of its compartment toward the lone guppy and measured how close the model came before the guppy swam away. They then determined the relationship between that distance and the color index score (Figure 19.3C). To determine the statistical significance of these relationships, the scientists determined the correlation (rS) of the ranked values and the probability of seeing a correlation this large. Because both p-values (P in Figure 19.3B and C) are less than 0.05, the relationships between the variables are statistically significant.

In one final experiment, Dugatkin and Godin let females observe pairs of males, one bright and one drab, in the presence or absence of a predator. They simulated boldness and timidity by placing males in clear plastic tubes and holding them still, or moving them closer to the predator. In half of the trials, boldness was simulated in the bright male, and timidity was simulated in the drab male. In the other half of the trials, boldness was simulated in the drab male, and timidity was simulated in the bright male. Males were then placed with the female, and they observed courtship behavior (Figure 19.4). Bio-Math Exploration 19.1 explains how statistical significance of mating preferences can be quantified.

Figure 19.4 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. "Bright" on the x-axis indicates the bright male in the bright/drab pair of males was the one simulated to be bold. "Drab" means the drab male was simulated to be bold. Purple bars indicate the number of bright males chosen by females, and teal bars indicate number of drab males chosen in a set of trials. Pairs of purple/teal bars add up to twenty trials, which is the sample size for each mate choice experiment. Data from Godin and Dugatkin, 1996, Figure 4, © (1996) National Academy of Sciences, U.S.A
3

 

In the presence of female guppies, bright guppies are more likely than drab guppies to swim toward and inspect potential predators. The bright guppies are also more likely to flee from predators before the predator gets too close. This may enhance their survival, and may be something they were unable to do in the first experiment, where survival of bold guppies was zero. The variation in behavioral tendencies is associated with variation in appearance, as colorful males were 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. However, in choice tests, 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 may be able to assess male boldness by examination of their color alone.

In Dugatkin’s first experiment, you discovered that bold males were selected against—that is, their survival was very low because of their tendency to inspect predators. You may wonder how individuals with that behavior remained in the population, or why the population did not consist of all timid males. Now you have discovered that boldness is preferred by females. In addition, variation in color pattern, which is associated with behavior, is used by females as a signal for potential boldness when they cannot actually observe how a male would behave if there were a predator present. Bold males inspect more often, which may signal to the predator that it has been spotted, and this behavior allows bold individuals to flee an approaching predator earlier than a timid individual. Females may prefer these types of males because bright color indicates their boldness, and boldness is related to their ability to detect predators. Bold behavior may be associated with other aspects of success, such as higher rates of feeding. The value of this to the female is that bold, healthy males may contribute more advantageous genes to the female’s offspring. Phenotypes that remain in a population are maintained by providing an advantage to the possessor, whereas phenotypes selected against reduce the ability to survive or reproduce.

Even though females prefer bold, bright males, timid males are still able to mate, which helps explain why this characteristic remains. Dugatkin and Godin found that the correlation between brightness and boldness was not perfect; not all bold males were brightly colored. Multiple genes may code for proteins involved in those characteristics, and the environment in which a guppy develops may also contribute to that variation. Natural selection is not perfect, and there are many factors that affect the success or failure of any one individual. Timid males may have higher survival than bold males under some conditions. If all timid, drab males were eliminated from the guppy population, the remaining population of bright, bold males will be less variable, and this may have negative consequences in an environment with a high abundance of predators. Such a population of bold and bright guppies could be selected against and face extinction.

Natural selection on a discrete trait

Most sexually reproducing populations contain individuals that display variable behaviors, structures, molecules, and other phenotypes, like the guppies you just studied. Doug Schemske and Paulette Bierzychudek used another variable population and investigated whether that variability was maintained by natural selection. Schemske and Bierzychudek worked with desert snow ( Linanthus parryae ), a small annual plant that has a flower color dimorphism; some plants have blue flowers and others have white flowers. It lives in the deserts of southwest North America. Flower color is determined by a single gene, with blue dominant to white. {Connections: Mendelian inheritance is discussed in Sections 3.1 and 3.2.} Populations of this plant can contain all blue flowered individuals, all white flowered individuals, or a mix of the two.

Schemske and Bierzychudek asked how selection acted on individuals that had different flower color. Individual populations of desert snow tend to be stable in flower color; that is, the frequencies of phenotypes found in one area remains constant over time. They found a shallow ravine where the plants on one side were predominantly blue flowered and the plants on the other side were predominantly white flowered. Over a period of 7 years, they sampled plots along two lines that crossed from one side of the ravine to the other, one at the northern end of the ravine and one at the southern end (Figures 19.5A and C). The flower color data were averaged over many annual censuses.

Figure 19.5 Spatial pattern of flower color along a north (A) and south (C) line that cut through a ravine and estimates of frequencies for four allozymes along the north (B) and south (D) lines. The vertical bar near the center of each panel marks the position of the ravine that divides the populations. From Schemske and Bierzychudek, Figure 4, Copyright 2007, John Wiley and Sons.

The researchers hypothesized that there was intense local selection for flower color, such that blue flowers were favored on one side of the ravine and white on the other. To support this hypothesis, the researchers tested four other genes, predicting that other genetic loci would show no pattern across the ravine if selection were only on flower color; the other genes were not selected for or against (Figures 19.5B and D). To determine the frequencies of alleles of the four genetic loci, the scientists collected individual desert snow plants across the ravine. They extracted enzymes and separated allozymes for the four genes using electrophoresis. The frequency of the most common allozyme produced by each gene across the ravine is plotted in Figures 19.5B and D. 

The scientists also planted both white and blue flowered plants in plots on both sides of the ravine to determine their seed production success in the two habitats (Figure 19.6). Note that 1995 was wetter than average, and 1996 was drier than average.

Finally, Schemske and Bierzychudek collected data on environmental factors on the two sides of the ravine to determine what selective factors there might be in the two habitats. They looked at the other plants in the community, which are potential competitors, as well as soil properties (Figure 19.7).

Figure 19.6 Mean seed production (± 1 SE) 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; SE, standard error. From Schemske and Bierzychudek, Figure 6, Copyright 2007, John Wiley and Sons.

Figure 19.7 Environmental variation of desert snow on two sides of a ravine. A, Area covered for ten 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. Ca, calcium; K, potassium; Mg, magnesium; Na, sodium; P, phosphorus; S, sulfur; EC, electroconductivity; %OM, percent organic matter; NH4-N + NO3-N, total nitrogen. * = 0.05, ** = 0.01, *** = 0.001, **** = 0.0001 probability of the observed differences if the null hypothesis of no difference is true. From Schemske and Bierzychudek, Figures 7 & 8, Copyright 2007, John Wiley and Sons.

The scope of Schemske and Bierzychudek’s study, in terms of the area studied and the number of desert snow populations studied, was small. Within the two small populations studied, however, the researchers established that there was a clear difference in flower color on each side of the ravine and that the observed differences persisted over time. That is suggestive of two populations adapted to two specific habitats, although limited dispersal could also explain the distribution of flower types.

Because flower color is determined by one gene, examination of flower color can lead to estimates of allele frequencies at that genetic locus. On the east side of the ravine, where white flowered plants predominate, almost 100% of the alleles in the population were for white flower color. You know this because that characteristic is recessive. On the west side, if all the blue-flowered individuals were heterozygous, the frequency of the white flowered allele would be 50%, and if all blue flowered individuals were homozygous dominant, that frequency would be 0%. {Connection: Recessive and dominant genes are explored and genetic loci are defined in Section 3.1.} We don’t know the exact percentage in the blue flowered populations, but you observed that the frequency of one allele 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 ravine varied little. That suggests no selection for or against those enzymes and that only flower color is selected for.

In the experiment where seeds from plants of each color were planted on both sides of the ravine, Schemske and Bierzychudek found that there were differences in seed production, although they were 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. On the blue flowered plant side, blue and white flowered plants produced equal numbers of seeds per plant. 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. White flowered plants are typically more successful in wet years and the blue in dry years. Plant success is also tied to other environmental conditions, evidenced by the different species of plants present and the different soil conditions that plants experience on either side of the ravine.

How the soil differences came to be so great over such a small spatial scale is unknown. The soil environment or some other unknown, unmeasured factor could have then given rise to variation in plant community composition. Either of these factors, soil or 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. However, Schemske and Bierzychudek showed that ecological factors can and do vary, and this variation leads to natural selection on a local scale over short periods of time. Natural selection can eliminate certain characteristics from a population, thereby reducing variation. But natural selection can also maintain variable characteristics by favoring certain types in different local habitats. We now turn our attention to how variation among individuals in a population leads to descent with modification and speciation over much longer periods of time.

 

Annotate

19.2 How will communities respond to climate change?

· Context: Predicted future climate change may alter communities and species’ interactions.

· Major theme: Species evolve in the context of their environment.

· Bottom line: As global climate changes, species will evolve, leading to changes in entire communities and the interactions therein.

Biology Learning Objectives · Describe how global climate is changing. · Evaluate how organisms are responding evolutionarily to global climate change.
Figure 19.8 Observed continental and global-scale changes in surface temperature with results simulated by climate models using natural and human-caused factors. Ten-year averages are shown in the black line. Lines are dashed where measurements were taken in less than 50% of the area. Pink and blue shaded bands show the range in which 90% of the predictions from computer simulations fell. Figure SPM 4, IPCC, 2007: Summary for Policymakers. In: Climate Change 2007

The Intergovernmental Panel on Climate Change (IPCC), sanctioned by the United Nations and comprised of hundreds of climatologists and policymakers from many countries, comes out with a report on climate change every 5 to 6 years. The most recent report, published in 2007, stated that the average global temperature is rising on every continent and in the oceans (Figure 19.8) and that it is very likely that humans are contributing to the change.

The IPCC concluded this using observed data, theory, and computer models. In Figure 19.8 you see that the observed data are very close to or within the range of results obtained from computer models using both natural and human-caused factors, whereas models using only natural factors, such as solar activity and volcanoes, did not fit the observed data as well, especially during the last half of the 20th century. Human-caused factors associated with temperature increases include burning of fossil fuels, deforestation, and altered land uses, all of which affect the production of greenhouse gases. The IPCC projects that temperatures will continue to rise and the rate of increase will be dependent upon our actions to curb production of greenhouse gases. Global average temperature at the end of the 21st century is predicted to be 2o to 6o C warmer than the current global average temperature. For the next couple of decades, warming of about 0.2° C per decade is predicted. Other aspects of climate also change when average temperatures increase. {Connections: The impact of climate change on ecological system homeostasis is discussed in Section 30.3.} For instance, some regions of the planet are predicted to receive more precipitation, others less. Some areas will be subject to stronger and more frequent storms, including hurricanes.

Although climate has changed often during the history of the planet, the changes occurring now are rapid and global, and they are outside the range experienced by humans since before the development of agriculture. Ecological systems have begun to change during the past few decades, and studies have documented changes in species interactions, seasonal activity patterns, and expansion of geographic ranges. In this section, you will see some data on evolutionary responses of species to these climate changes.

Video on how climate change can increase agricultural problems

[if IE 7]> <html class="ie ie7 lt-ie10 lt-ie9" lang="en-US" prefix="og: http://ogp.me/ns#"> <![endif][if IE 8]> <html class="ie ie8 lt-ie10 lt-ie9" lang="en-US" prefix="og: http://ogp.me/ns#"> <![endif][if IE 9]> <html class="ie ie9 lt-ie10" lang="en-US" prefix="og: http://ogp.me/ns#"> <![endif][if !(IE 7) | !(IE 8) ]><!<![endif] [if lt IE 9]> <div class="ie-alert" style="display: none;"> <h5>We notice that you're currently using Internet Explorer version 8 or earlier. To ensure a better experience on our site, we recommend using a recent version of Internet Explorer, <nobr><a href="http://www.google.com/chrome">Google Chrome</a></nobr> or <nobr><a href="http://www.mozilla.org/en-US/firefox/new/">Mozilla Firefox</a>.</nobr></h5> </div> <![endif]

==== Sitewide Menu - Button (see off-canvas panel after </header>) ====

KQED Menu

==== Future of You Logo and Nav Menu (Dropdown) ==== ==== Science Logo and Nav Menu (Expanded) ====

1 KQED Science

4 NEWS

5 PROGRAMS & BLOGS

6 EDUCATION RESOURCES

==== NPR, PBS Logos ====

7 NPR

8 PBS

==== Donate Button and Sitewide Search ====

8 Donate

9 Search

/inner

==== Sitewide Menu - Off-Canvas Panel ====

CLOSE

Popular

1 KQED HOME

2 LISTEN LIVE

3 WATCH EPISODES

4 PODCASTS

5 RADIO SCHEDULES

6 TV SCHEDULES

7 MOBILE/APPS

8 DONATE

Sections

· RADIO

· TV

· NEWS

· ARTS

· FOOD

· SCIENCE

· EDUCATION

· SUPPORT KQED

· ABOUT KQED

[if gte IE 9]><! KQED <![endif]

Public Media for Northern California

QUEST

Search

Skip to content

· TOPICS
COLLECTIONS
STEM CAREERS

#masthead

HEAT AND HARVEST

Some Bugs Like it Hot: Climate Change and Agricultural Pests

Potato-tomato psyllid. Photo: Gary McDonald.

By Gabriela Quirós, KQED Science

SEP. 28, 2012

SHARE

.entry-meta

.inner

.entry-header

 

Potato grower Brian Kirschenmann bent over the deep green plants in his field in New Cuyama, nestled in a verdant valley an hour southwest of Bakersfield, and looked at the bottom of each leaf.

Next to him, his crop advisor Gary Toschi examined a leaf under a magnifying glass.

“That one is active,” said Toschi.

An adult potato-tomato psyllid surrounded by young nymphs. Photo: Jeffrey Bradshaw, Univ. of Nebraska, Lincoln.

He and Kirschenmann had found a round, bright green insect, about the size of the comma on a computer keyboard. The tiny pest is called a potato-tomato psyllid, and both the young nymphs like the one they found, and the adults, which are shaped like a cicada, wreak havoc on potatoes, as well as tomatoes, peppers and about 40 other crops.

In potatoes, they suck the plant dry. And what’s worse, the pest also can transmit a disease that ruins potato chips – a $6 billion business in the United States, despite the bad rap chips get from nutritionists. The disease transmitted by potato-tomato psyllids gives chips a burnt flavor and causes them to develop brown streaks, which is why the disease is known as “zebra chip.”

Potatoes infected with zebra chip disease streak when they’re fried. Photo: John Trumble.

In 2008, Kirschenmann lost $250,000, when one of his potato fields in Kern County was infected with zebra chip and Frito Lay wouldn’t buy his chipper potatoes. Though potatoes aren’t one of California’s main crops, in Kern County they’re among the top 10 in value, to the tune of $130 million.

Zebra chip disease hasn’t caused widespread losses in California, but the state’s potato growers are worried, especially after South Korea banned potato imports from Washington, Oregon and Idaho in August, out of concern over the disease. The ban is costing producers in those states some $8 million.

“That’s my biggest fear,” said Kirschenmann, who grows 5,700 acres of potatoes in Kern County and New Cuyama and is a member of the United States Potato Board. Though California doesn’t export potatoes to South Korea, Kirschenmann does sell part of his crop to Guatemala and the Dominican Republic.

Potato grower Brian Kirschenmann.

California has had potato-tomato psyllids for more than 100 years. What makes them a new problem for growers is that now they don’t just live in the state during the warmer months; they also spend the winter.

“Our temperatures have increased by 2 to 3 degrees Fahrenheit, and that seems to be enough to keep them from being frozen out during the winter,” said entomologist John Trumble, of the University of California, Riverside. “I suspect that global warming is at least playing a role in this particular insect’s spread into California.”

Insects can’t produce their own heat, the way mammals do, so most of them do better in warmer temperatures. Around the world, scientists have started to document changes in insect behavior, as a result of climate change.

Entomologist John Trumble, from UC Riverside, says warmer temperatures are making a pest of potatoes and tomatoes more abundant in California.

In Spain, the European grapevine moth is flying out earlier in the summer and reproducing more abundantly than it did 20 years ago. On Tanzania’s Mt. Kilimanjaro, malaria mosquitoes are moving farther up the mountain. And in Japan, a pest called the green stink bug, that damages rice and soybeans, is expanding its range northward.

“This is not speculative, this is not something that we would predict,” said Trumble. “This is what’s happening now.”

When Trumble discovered, in the year 2000, that potato-tomato psyllids had spent the winter near one of his research tomato fields in Irvine, he knew this change in the insect’s behavior was bad news: it meant the pest could begin attacking crops early in the growing season.

“It’s much more dangerous for the grower,” said Trumble, “because early infestation in a crop oftentimes leads to much more damage than if the pest occurs late in the crop.”

When spring temperatures get warm, this sends a cue to insects to begin their development, said entomologist Peter Oboyski, manager of collections at the University of California, Berkeley’s Essig Museum of Entomology.

“If the season gets warmer earlier, that gives insects that much more time to develop, that much more time to eat a plant, that much more time to produce another generation,” said Oboyski.

Potato-tomato psyllid. Photo: Jack Kelly Clark, courtesy University of California Statewide IPM Program.

But how did UC Riverside’s John Trumble know that he was seeing a new behavior in the psyllid?

To figure out if spending the winter in California was a new behavior, Trumble turned to the historical record. He was fortunate that scientists in California have been collecting psyllids and making detailed notes about their habits since the 1880s. Through this information, he concluded that he was seeing something new.

“Every 30 years, since about 1900, it’s moved into California,” said Trumble, “and we would find it, it would be here for six months to a year, and then it would disappear, presumably because it got too cold.”

That pattern changed radically in 2000, with the psyllid spending the winter in Trumble’s field in Irvine. And the pest has since overwintered in locations further and further north.

In 2004, tomato growers discovered psyllids had overwintered in Hollister. In 2012, scientists found they had spent the winter in the Washington-Oregon-Idaho area, where half of the country’s potatoes are grown. And this year, the pest also appeared in Manitoba, Canada, early in the growing season, a sign that it might have spent the winter there.

“As temperatures warm up in California and across the United States,” said Trumble, “these insects will be able to overwinter further and further north.”

Meanwhile, down south, farmers have already been hit hard by the pest. In Mexico’s Baja California, the psyllids destroyed 85 percent of the tomato crop in 2001, said Trumble. Partly in response to the new pest, California farmers who grow tomatoes in Baja California have moved much of their production inside screened enclosures.

Spraying for psyllids is costing California potato growers about $75 per acre, said Kirschenmann.

Potato field in New Cuyama Valley, an hour southwest of Bakersfield. Potato-tomato psyllids have been found nearby. Photos: Gabriela Quirós.

This need for additional spraying has Trumble concerned. Since the 1970s, he and other scientists around the state have worked with growers to reduce pesticide use in conventionally-grown crops. The results have been dramatic, he said. Tomato growers, for example, cut spraying by half in the 1990s.

“Less pesticide use means less concern by the consumers for pesticide residue. We use less fossil fuel; we have fewer volatile organic compounds that appear in the atmosphere; it reduces smog. It’s a real win-win for everybody in California,” said Trumble.

But warmer temperatures, and the pests that thrive in them, now threaten to undermine these gains.

“So if you have an insect with multiple generations, you get more generations. If you’ve got an insect that occurs early in a crop, it will occur earlier in the crop, and faster,” said Trumble. “So all of these things are desperately in need of additional research.”

A shorter version of this video story is part of the 30-minute documentary Heat and Harvest, a co-production of KQED and the Center for Investigative Reporting, that airs on KQED and PBS stations around California on Friday September 28 at 7:30 pm. Check your local listings.

EXPLORE: CLIMATE, ENVIRONMENT, FOOD, AGRICULTURAL PEST, CLIMATE CHANGE, HEAT AND HARVEST, JOHN TRUMBLE, PESTICIDES, POTATO, POTATO-TOMATO PSYLLID, QUEST NORTHERN CALIFORNIA, RIVERSIDE, SUSTAINABLE FOOD, TELEVISION, TOMATO, UNIVERSITY OF CALIFORNIA, VIDEO, ZEBRA CHIP DISEASE

·

0 Comments

RELATED

Heat, Salt and Pests Threaten California Fields

Dry and Salted

Heat and Harvest - the documentary

On GMO Labeling, Oregon and Colorado Learn from California Ballot Defeat

Uncool Cherries

Powered by

.entry-content

AUTHOR

#author-avatar

GABRIELA QUIRÓS

Gabriela Quirós is a video producer for KQED Science and the coordinating producer for Deep Look. She started her journalism career more than 20 years ago as a newspaper reporter in Costa Rica, where she grew up. She won two national reporting awards there for series on C-sections and organic agriculture, and developed a life-long interest in health reporting. She moved to the Bay Area in 1996 to study documentary filmmaking at the University of California-Berkeley, where she received master’s degrees in journalism and Latin American studies. She joined KQED as a TV producer when its science series QUEST started in 2006 and has covered everything from Alzheimer’s to bee die-offs to dark energy. She has shared two regional Emmy Awards, and nine of her stories have been nominated for the award as well. She has shared awards from the Jackson Hole Wildlife Film Festival and the national Society of Professional Journalists. Independent from her work in KQED's science unit, she produced and directed the hour-long documentary Beautiful Sin, about the surprising story of how Costa Rica became the only country in the world to outlaw in vitro fertilization. The film started to air nationally on public television stations in 2015.

VIEW ALL POSTS BY THIS AUTHOR

#author-link

#author-description

#entry-author-info

SPONSORED BY

Default ads code appears if no site recongnized

Become a KQED sponsor

.widget-ads

.ad-sidebar

#post-##

Post navigation

.nav-links

.navigation

#content

#primary

#main

ABOUT QUEST

KQED's team of science educators and producers bring you engaging, relevant science and engineering resources for educators, students and lifelong learners.

QUEST is a project of KQED Science and KQED Education.

ABOUT KQED

· About

· Jobs

· Internships

· Donate to KQED

· Website help

QUEST IS SUPPORTED BY:

Figure 19.9 A, Long-winged form of the bush cricket Conocephalus discolor. B, Short-winged form of Metrioptera roeselii. C, Distribution of C. discolor. Blue circles are locations where the cricket was first recorded between 1961 and 1987, yellow circles are locations where it was first recorded between 1988 and 1996, and red circles are locations where it was first recorded between 1997 and 1999. D, Distribution of M. roeselii; circle colors are as in C. E, Proportion of long-winged C. discolor individuals in populations sampled in 2000, according to year a population was first recorded. F, Same as E, but for M. roeselii. A, From http://en.wikipedia.org/wiki/File:Long-winged_Conehead_Conocephalus_ discolor_(9878193476).jpg, Author Ian Kirk from Braodstone, Dorset, UK. This file is licensed under the Creative Commons Attribution 2.0 Generic license. B, From http://www.pbase.com/tmurray74/image/52841449, Author Richard Bartz, Munich aka Makro Freak. This file is licensed under the Creative Commons Attribution-Share Alike 2.5 Generic license. C-F, Thomas et al., 2001, Figure 2, reprinted by permission from Macmillan Publishers Ltd.

Thomas and colleagues studied several species of insects in England. Two of the species they analyzed were bush crickets, which is a type of grasshopper (Figure 19.9A and B). The scientists tracked the year each species and population was first recorded throughout its current range, using published records of the species. They found that the range of each species has changed over time (Figure 19.9C and D), becoming larger for each species. The expansion of geographic range, noted for many species of insects, may be related to evolutionary changes in species. Not all species will respond in the same way; in fact, some species may become extinct as climate changes. In this section, you are considering potential evolutionary responses to climate change.

In the case of the bush crickets, both species have a long-winged form that had been relatively rare in sampled populations in the past, and they determined the proportion of long-winged individuals in the populations, and analyzed that proportion according to the date a population was first discovered (Figure 19.9E and F). Populations that had been known for longer periods of time tended to have lower proportions of long-winged forms.

 

Both species of bush cricket are spreading northwards and inland from southern coastal regions. In addition, the frequency of long-winged individuals has changed over time, and some recently discovered populations have the highest frequencies. Those populations are also at the edges of the expanding geographic range. The increased frequencies of long-winged individuals in recently established populations could mean that environmental variables have changed and are affecting the development of immature crickets, or there could be genetic differences that affect the temperature a cricket must experience when developing before it will become a long-winged adult.

Thomas and colleagues concluded that, even in the face of climate change, there are likely to be genetic, evolutionary changes in the populations because high population density plays a strong role in development of the long-winged form, and newly founded populations typically have both low population densities and high proportions of long-winged individuals. In addition, if the crickets expand their ranges as temperatures rise, we would expect to observe similar proportions of long-winged individuals in populations that are in the north now as would have been observed in the south decades earlier. That is not the case. Thomas and colleagues concluded that there were evolutionary changes in these two species that facilitated the expansion of their geographic ranges. They further concluded that dispersal of long-winged individuals can speed up range expansion disproportionately as they increase in the population.

Evolutionary response to changing rainfall

Changing weather patterns are another aspect of climate change. As temperatures increase, rainfall patterns change. {Connections: Other environmental changes associated with warming temperatures are explored in Section 30.3.} Populations may evolve in response to a warming trend, as you have seen, but they may also adapt to long-term changes in precipitation patterns. It is important to separate growth or development responses that are not genetic from those that are. Warmer or wetter conditions earlier in spring could simply cause plants to flower earlier or organisms to grow faster. Alternatively, there could be selection for individuals that grow or develop faster under these new conditions. Researchers must be able to determine whether there have been genetic changes in order to conclude that evolution has occurred.

Steven Franks and his colleagues studied a small flowering plant called wild mustard or field mustard ( Brassica rapa ). Many organisms, including mustard go through a dormant state, such as a seed or an egg. Franks and colleagues took advantage of this and used ancestral and descendant seeds in the same experiment to separate a growth response from an evolutionary response to changing environmental conditions. Seeds were collected in 1997 and 2004 from plants living in two sites in California, a site with sandy soils that dry quickly and a marsh site with soils that retain moisture after precipitation. The years between 2000 and 2004 were unusually dry at both sites, which led to shortened growing seasons for many plants. The plant populations may have evolved between 1997 and 2004 in response to the changing climate. Franks and colleagues predicted that shortened growing seasons would select for earlier flowering times, because plants that flowered earlier would be at a selective advantage to plants that flowered later during the dry season. They predicted that descendant plants from the dry site would show a greater evolutionary response due to the increased stress from growing in drier soils.

Franks and his colleagues grew plants from seeds that had been produced at different times and at both sites. The researchers crossbred the new plants to produce seeds that were offspring of ancestral 1997 parent plants (1997 plants bred with 1997 plants) and descendant 2004 parent plants (2004 plants bred with 2004 plants). The scientists performed these crosses for plants from each of the two sites. The seeds from these crosses were planted in pots in a greenhouse. Each of 300 pots received seeds from each site and each time: two from the ancestor (1997) cross (one from wet soil and one from dry) and two from the descendant (2004) cross (one from wet soil and one from dry), resulting in four plants per pot.

For the first 33 days pots were watered daily. Researchers stopped watering one-third of the pots at that point, representing a short rainy season. They then stopped watering the next third of the pots after day 51, representing a medium season; and then they stopped watering the last third after day 81, representing a long season. They recorded the day that each seed germinated and each plant flowered. The trend in flowering times for short and medium seasons was similar to that of the long season plants shown in Figure 19.10. Plant mortality was also tracked (Figure 19.11).

Figure 19.10 Box plots of time to first flowering in wild mustard plants from the long season treatment. Wet refers to the high soil moisture site of origin, and dry refers to the sandy soil site of origin. 97 refers to plants from the ancestral cross (1997), and 04 refers to plants from the descendant (2004) cross. The center bar is the median, and the boxes, lines; and dots represent the 25th to 75th, 10th to 90th, and 5th to 95th percentiles, respectively. From Franks et al., 2008, Figure 2, Copyright (2007) National Academy of Sciences, U.S.A.

Figure 19.11 Mean (+ 1 SE) percent of wild mustard plants from each site of origin surviving in the different treatments. Wet refers to the high soil moisture site or origin, and dry refers to the sandy soil site of origin. 97 and 04 are as in Figure 19.10. Length of season refers to the length of time plants received daily watering. From Franks et al., 2008, Figure 3,Copyright (2007) National Academy of Sciences, U.S.A.

Table 19.1 Heritability of flowering times in wild mustard plants from two sites of origin. Data from Franks et al. 2008.

In a second experiment designed to assess heritability in flowering times, Franks and his colleagues planted seeds from the ancestral (1997) parents in the same pots. As in the drought tolerance experiment, they measured the time to flowering. They estimated heritability as the slope of the line that best fits the graph of mean flowering time of a set of offspring versus the mean flowering time of the parents (Table 19.1). {Connections: Best fit lines between parent and offspring traits are explained in Bio-Math Exploration 16.1.} If all variation in flowering time was due to differences in environmental factors, the slope of the line would be zero, and there would be no correlation between mean offspring flowering time and mean parent flowering time. The higher the heritability, the more variation in offspring flowering times is attributable to variation in parent flowering times, and thus offspring inherited their flowering time from their parents. In addition, if the confidence interval range did not include 0, then the researchers could conclude that at least some of the variation was due to inheritance of traits. {Connections: Confidence intervals are explained in Bio-Math Exploration 19.4.}

 

The median flowering time for plants from the dry soil site were all earlier than plants from the wet soil site, regardless of whether the plants were ancestral (1997) or descendant (2004). The researchers found large differences in median flowering times for plants from the wet soil site, depending upon whether the plants were offspring of ancestral or descendant parents and depending upon their site of origin. Descendant plants that originated from the post-drought time (2004) flowered earlier than plants from the pre-drought time (1997). The major environmental change between 1997 and 2004 was a change to much less than average precipitation, leading to shorter rainy seasons. This led to a change in the population from the wet soil site that made it look more similar to the plants from the dry soil site. Plants from the dry soil site had evolved to flower earlier because the ancestral plants had already experienced dry conditions; thus, they did not respond to the dry period between 2000 and 2004 as the plants from the wet soil site did.

Mortality differences among the treatments were profound. Plants from 1997 wet site parents exposed to short and medium wet seasons had very low survival, whereas plants from 2004 wet site parents had much higher survival. When seasons are short, plants that delay flowering are unable to produce seeds before dry conditions kill them. The higher survival of 2004 offspring indicates that this population of plants had evolved to the drier conditions in as short a time as 7 years. Survival of ancestral and descendant plants from the dry soil type was always high, regardless of the length of the wet season. This suggests local adaptation, that is, there were genetic differences between the two populations. One population had already evolved to dry conditions, and the other one evolved during the period between 1997 and 2004.

Genetic differences over time and between the two populations are also indicated by the heritability estimates. Estimates greater than zero indicate that there is variation in flowering times caused by genetic changes. Heritability in these wild mustard populations was high, that is, up to almost half of the observed variation in flowering times was due to variation in parent flowering times. Wild mustard exposed to drier conditions appear to have evolved and be better adapted to short wet seasons. You have concluded that rapid evolution by natural selection can occur if the selective force is strong and the heritability is high.

The examples presented here demonstrate that evolution can occur quite rapidly as climate changes. Climate change involves more than just warming, and organisms can respond evolutionarily to a variety of climate factors. Species can respond to more than one factor simultaneously. For the response to be evolutionary, there must be heritable genetic variation for the trait in question. The changes in species affect entire ecological systems, because evolutionary changes may allow for range expansion or changes in interactions among species. Species that do not respond evolutionarily may go extinct, which also will affect ecological systems.

 

19.3 When are two isolated populations not isolated?

· Context: Gene flow links populations with individual dispersal and mating, and it is a mechanism of evolution.

· Major themes: Life continues to evolve within a changing environment, and organisms can be linked by lines of descent from common ancestry.

· Bottom line: Dispersal and mixing of gene pools leads to gene flow and prevents speciation.

Biology Learning Objectives · Evaluate the evolutionary effects of gene flow on populations and variation in populations. · Evaluate how the structure of a population affects its evolution and can affect the strength of gene flow on evolution of populations.

Population structures are often complex in time and space. That is, populations of the same species may be geographically separated from one another, or they may become separated over time due to geologic events, such as formation of mountains or erosion of canyons. {Connections: Population structure is further examined in Section 26.2.} In this section, you will study several examples of spatial structure and dispersal to better understand how dispersal can maintain genetic similarity of populations.

Consider a species of squirrels that lives on either side of the Colorado River. Although an individual squirrel may occasionally cross the river, once the Grand Canyon was hollowed out by the river squirrels were much less likely to cross that divide, and they became two separate populations. Over time, those separated populations evolved differences in the patterns of their fur color and are recognized by experts as two subspecies of the same species. Might they become two separate species, given enough time? Possibly, but time is only one factor in a speciation event, as you will learn in Section 20.1, where speciation of orchids and bats was demonstrated to have taken place over millennia. If two populations diverge enough, a speciation event may occur. In this section, we will examine a mechanism of evolution that can counter the divergence and spatial separation of two populations.

Some populations have limited dispersal

To understand how evolutionary mechanisms are affected by movement of individuals between populations, you will explore the population structure of two species that disperse over relatively small spatial scales. Eric Kay and Rytas Vilgalys studied the population of a fungus, the oyster mushroom, which feeds on and decomposes logs in moist forested areas. Fungi typically grow as hyphae on or in solid substrates and are not observed by most people. Many people notice the mushroom, which is the fleshy, spore-bearing fruiting body of a fungus, typically produced aboveground or on a food source, such as a decomposing log. Mushrooms contain spores that, when released, lead to dispersal of the fungus.

Figure 19.12 Spatial structure of a fungus population in a forest, showing downed logs (squares and triangles), and the number of distinct genetic types of fungus at each log. Solid lines indicate locations of streams, and shading indicates non-forested areas. The shaded square in the middle left is log R, which is shown in detail in Figure 19.13. From Kay and Vilgalys Figure 2, Reprinted with permission from Mycologia. Copyyright The Mycological Society of America. .

Kay and Vilgalys mapped the location of each log in a forest that had sprouting mushrooms, recorded the position of clusters of mushrooms on each log, and collected samples of mushrooms. To determine how many distinct individuals had colonized each log, they performed genetic testing. Counting individual mushrooms is not a good indicator of how many individuals live on a log, because one individual fungus in a log can grow multiple mushrooms.

Kay and Vilgalys found between 1 and 16 distinct individuals on each of 21 logs, for a total of 53 individual fungi. The researchers mapped the location of rotting logs and noted the number of individual fungi on each log (Figure 19.12).

The scientists compared the genetic similarity of individuals and their distribution relative to related individuals. They did this for the forest, but also for each individual log (Figure 19.13). One individual fungus that colonizes a log through dispersal might grow to take over that log through asexual reproduction, or other individuals may arrive through dispersal. The fungi in this forest can be examined at the spatial scale of the log (one or several individuals), or at the scale of the whole forest (the population).

The movement and genetic relatedness of individuals within the population are important factors in determining the population structure and the evolution of the population. If logs were farther apart than spores generally travel, new populations could form, having become isolated by distance. This could affect the genetic relatedness and the evolution of the species.

Figure 19.13 Distribution of genetic types in a fungus inhabiting a rotting log whose location is indicated by the red square in Figure 19.12. Groups of genetically similar individuals are circled. From Kay and Vilgalys, Figure 3, Reprinted with permission from Mycologia. Copyyright The Mycological Society of America. .

Limited dispersal affects population structure and genetic relatedness. Related oyster fungi were more likely to be found near each other, usually on the same log, although unrelated individuals also can inhabit the same log. On a single log there are often multiple individual fungi of the same genotype (Figure 19.13), indicating localized dispersal. Isolation could occur if individuals disperse only short distances and mate only with individuals within a small geographic range. Most spores land very close to the mushroom from which they dispersed, but a small percentage is carried a much longer distance by wind, water, or animals. This happens infrequently. Fungi that occupy logs nowhere near other logs are evidence of limited long range dispersal.

For species spread across a large geographic area, individuals in one part of a species’ range may not encounter individuals in another part of the range. A portion of the population does not greatly intermingle with other portions of the population. Other species besides the fungus you just examined may have localized breeding and dispersal. This lack of gene flow can affect the genetic isolation of populations.

Figure 19.14 Groundsel (Senecio integerrimus), found in the Colorado Rockies. Note the beetle on the flower on the right. From http://en.wikipedia.org/wiki/File:Senecio_integerrimus_7466.JPG. This file is licensed under the Creative Commons Attribution-Share Alike 3.0 Unported, 2.5 Generic, 2.0 Generic and 1.0 Genericlicense. Author: Walter Siegmund, 2008.

Schmitt examined populations and their sizes in three species of sunflower-like plants, and how they were affected by bee and butterfly pollination (Figure 19.14). The flowers of these plants are large and showy with space for insects to land. Although individual flowers each have only a small amount of nectar, the grouping of flowers makes these plants attractive to a wide variety of pollinators with different feeding strategies. Insects often eat both pollen and nectar, which is a sugar solution produced by many flowers. Bees typically require a high rate of energy intake, consuming nectar and pollen and transferring pollen in the process. {Connections: Rate of energy intake and optimal foraging is discussed in Section 18.2.} Butterflies feed on nectar, they typically require less energy than bees, and they are also often looking for mates while flying and foraging. If you have the opportunity to observe either a bee or a butterfly foraging, watch for differences in their behaviors. Bees, as optimal foragers, attempt to maximize their rate of energy intake or minimize their time spent foraging, whereas butterflies are not just foraging—they are also looking for mates. What might they be maximizing?

Bee Pollinators of Southwest Virginia Crops (revised 6 June 2010) <div class="player-unavailable"><h1 class="message">An error occurred.</h1><div class="submessage"><a href="http://www.youtube.com/watch?v=l_etyEdu9fQ" target="_blank">Try watching this video on www.youtube.com</a>, or enable JavaScript if it is disabled in your browser.</div></div>

A variety of bees pollinating a diversity of flowers.

Schmitt hypothesized that for plants pollinated by several species, population structure would be significantly affected by different pollinator types. The pollinators’ feeding strategy would determine whether flower pollen would get transferred to another flower in a localized or more widespread pattern. She observed individual bees and butterflies as they foraged, and she recorded the number of flowers visited per plant, the distances flown between plants, and the total number of plants visited during an observation period. Schmitt constructed a relative frequency distribution graph of bumblebee and butterfly flight distances (Figure 19.15) and calculated mean flight distances and flowers visited (Figure 19.16). Bio-Math Exploration 19.2 explains how to interpret and use the relative frequency distribution.

Figure 19.15 Distribution of flight distances for bumblebees and butterflies visiting flowers. Figure 1 from Schmitt, 1980, reprinted with permission from Society for the Study of Evolution.	Figure 19.16 Mean flight distance (A) and number of flowers visited per plant (B) for bumblebees and butterflies. Data from Schmitt, 1980, Tables 1 and 2.

If populations are partitioned into small local clusters that are not genetically connected and natural selection acts differentially on those clusters, they may diverge evolutionarily. That is, genetic, morphological, behavioral, or physiological differences could arise such that individuals in one population no longer recognize individuals from another population as belonging to the same species. {Connections: Species recognition is discussed in Section 17.2 and 17.4. } A species’ dispersal ability will partly determine whether populations diverge genetically.

In the case of the sunflower-like plants, dispersal of pollen is determined by the pollinator. Schmitt found that about 92% of the bumblebee flights were less than 1 m in length (Figure 19.15A), whereas almost 50% of observed flights of butterflies were greater than 1 m in length, up to 50 meters (Figure 19.15B). This led to mean flight distances between flowers being much lower for bumblebees than butterflies (Figure 19.16A). Butterflies sometimes visited flowers close together, but they often flew a significant distance before stopping to collect nectar. Foraging bees visited a slightly higher number of flowers on a single plant than butterflies (Figure 19.16B). These observations make sense in light of what you know about foraging behavior of bees and butterflies. Recall that bees require a lot of energy, and they obtain this energy by visiting many flowers in rapid succession. Butterflies, on the other hand, are feeding and looking for mates, so they often fly right by available flowers.

In populations where bumblebees are the primary pollinator, Schmitt concluded that population clusters would be small, both in numbers of individuals and area occupied. When butterflies are the pollinator, populations are predicted to be more spread out and to be connected genetically, because gene flow and mating between widely separated individuals would be much more substantial.

Dispersal links geographic and genetic distance

You explored genetic similarity and population structure in the oyster fungus. To further explore how population spatial structure, genetic relatedness, and evolution are related, you will now examine the relationship between geographic and genetic distance. In the first example, the population structure and genetic relatedness of individuals of the bladder campion, Silene vulgaris, was studied by Olson and McCauley. Bladder campion is a short-lived plant common to roadsides in the eastern United States. It is a plant pollinated by insects, which is its mechanism for long-range dispersal. {Connections: Pollination is examined in Section 17.4.} Olson and McCauley described the patterns of diversity in the mitochondrial genome in populations of this plant in Virginia. {Connections: Mitochondria are discussed in Section 10.3.} They located 18 populations that contained as few as five individual plants to as many as 1,000. Each population was more than 500 meters from any other population. The researchers collected leaves from randomly selected plants scattered throughout the spatial extent of each population.

DNA was extracted from the leaves. Two genes from mitochondrial DNA were chopped up using restriction enzymes. These enzymes target and cut DNA when specific short sequences are detected by the enzyme, but due to variation in DNA from individual to individual, the enzymes cut the DNA at different locations in different individuals. This results in fragments of variable size. Scientists use the variation in those patterns as evidence of genetic variation among individuals.

Olson and McCauley found thirteen different allele combinations of the two genes among the 18 populations (Figure 19.17). Seven allele combinations were common and found in 5% or more of all individuals tested in three or more populations. The researchers calculated the genetic distance between pairs of populations. Bio-Math Exploration 19.3 helps you calculate genetic distances between any two populations in Figure 19.17. Olson and McCauley examined the effects of geographic distance between populations on the genetic distance and population structure, and they found that as geographic distance between pairs increased, genetic distance decreased, meaning the populations farther apart geographically were more similar genetically (correlation coefficient = - 0.246). This decrease was statistically significant, with a p-value of 0.002. This result was unexpected by the researchers; they predicted that populations farther apart would have higher genetic distances.

Figure 19.17 Distribution of 13 allele combinations (arbitrarily assigned a letter, shown in the insert box) among 18 populations of bladder campion. Each pie chart represents the geographic location and frequency of different allele combinations. Numbers are arbitrarily assigned to each population. From Olson and McCauley, 2002, Figure 2, © 2002, John Wiley and Sons.

In populations 6, 7, 10, and 11, all individuals possess the same allele combination. That is, there is no genetic variation among individuals at the genes tested. The genetic distance between populations 6 and 10 is 0 and between populations 7 and 11 is 1. The most common allele combination in bladder campion was f, which was both common and widespread. Combinations d and g were also quite widespread, although they were less common.

Bladder campion can disperse long distances—as evidenced by these widespread allele combinations and the counterintuitive observation that populations farther away from each other had lower genetic distances than populations closer together, on average. Your graph of the genetic versus geographic distances for populations 1 to 4 should show a linear trend with a slope of around - 0.02. Populations 1 and 2 are both similar to population 4, which is 7 to 9 km away from 1 and 2. In species that have widespread dispersal, it is quite possible that populations far apart from each other are similar genetically and that gene flow is acting on the populations.

Some species are restricted in their geographic ranges, but populations of some species span entire continents. Cabe examined European starlings ( Sternus vulgaris ) that have spread across North America with the goal of determining dispersal and population structure of this species. {Connections: Flocking in starlings is examined in Section 26.3.} Birds can be tracked by placing color-coded bands on their legs while they are still in the nest. The date and location of the nest are recorded. Later, when the birds are caught again, the date and location are recorded to determine the distance that they have dispersed after leaving the nest.

Figure 19.18 Frequency distribution of juvenile starling dispersal distances. Note that the x-axis has a discontinuous scale. From Cabe, 1999, Figure 1.

Cabe used banding data from the US Fish and Wildlife Service (USFWS) on starlings banded one year and caught the following year. In addition, Cabe collected tissue samples from starlings in four widely distributed states: California, Colorado, Vermont, and Virginia. He used these tissue samples to determine genotypic frequencies of 22 genes, which allowed him to calculate allele frequencies for each gene, as well as genetic distance of starlings in these four US states. {Connections: Genotypic frequencies are discussed in Sections 3.1 and 16.1. }

Cabe found that juvenile starlings moved anywhere from 12 to 2,623 km (Figure 19.18) between the time of leaving the nest and the subsequent breeding season.

He found that 16 of 22 genes contained only one allele in all starlings sampled in the four states. In other words, all individuals were homozygous for the same allele in those 16 genetic loci. Of the six remaining loci where more than one allele was found, Cabe calculated genetic distances of 0 for five of them. That is, all populations of starlings had the same percentages of each allele for those five genes. Only one out of 22 genes had multiple alleles that were found in different frequencies among starling populations in the four different states, and the estimate of variation among populations was low.

As with oyster fungi, most starlings disperse only a short distance away from their nest, relative to the maximum dispersal distance of this very mobile species. But a small percentage of them fly very far distances from where they were born. While dispersal abilities and distances are related to the mobility of species, you observed that species can disperse far enough to spread genes from one area to another but that each had a very different population structure. Even in populations separated by many kilometers, genetic similarities may exist. This indicates very little genetic isolation. A species may have populations separated spatially but connected genetically, and this helps maintain the species as a distinct entity. Clearly this is true for starlings. A female that flies 2,000 km and mates with a male in its new population mixes her genes with the population’s genes, and this decreases the genetic distance between the two populations.

The gene flow between populations is a mechanism of evolution that can prevent divergence of populations and speciation events. The flow of genetic material between populations helps maintain genetic similarity among individuals living in different areas. Mutations or adaptations that arise in one population can spread among populations through the process of gene flow. Gene flow can thus prevent speciation events from occurring. In the next section, you will examine how isolated populations may evolve in the absence of natural selection and gene flow.

 

19.4 Do populations evolve in the absence of natural selection?

· Context: Non-adaptive evolution can cause genetic diversity to be lost in populations.

· Major themes: Life continues to evolve within a changing environment, and organisms can be linked by lines of descent from common ancestry.

· Bottom line: Isolation of small, isolated populations can lead to evolutionary changes and loss of alleles through genetic drift.

Biology Learning Objectives · Evaluate gene flow and genetic drift in terms of their evolutionary effects on populations and variation in populations. · Evaluate how the structure of a population affects its evolution and can affect the strength of both gene flow and genetic drift on evolution of populations. · Explain how non-adaptive evolution, such as genetic drift, occurs, and describe the changes that accrue over time in the genome of a population. · Explain how speciation requires time and genetic isolation of populations.

You have constructed knowledge of how gene flow can reduce the genetic distance between populations or even disrupt divergence of populations into two species. Divergence of two isolated populations can occur because of variation in environmental selection pressures in two populations. However, consider two populations of the same species that are isolated from each other and live in environments with similar selective pressures. In the absence of natural selection, or adaptive evolution, can populations still diverge?

The conditions under which such non-adaptive evolution occurs may relate to the structure of the population, which you learned can be affected by gene flow, which connects populations. Populations separated in space may exchange very little, if any, genetic material. It is important to examine the population structure of a species when attempting to determine the evolutionary mechanisms at play, as you discovered. Can divergence of populations or even speciation occur in the absence of adaptive evolution and gene flow?

Population isolation affects genetic diversity

Consider species that live in mountainous regions, adapted to living at high altitudes, where populations are widely scattered. Such populations may be naturally fragmented with populations existing on different mountains widely separated geographically. Kuss and his colleagues studied three species of plants that live in the Swiss Alps: alpine willowherb ( Epilobium fleischeri ), a species of rose ( Geum reptans ), and yellow bellflower ( Campanula thyrsoides ). Populations of these plants are separated by anywhere from 5 to 30 kilometers. These plants are all insect pollinated and seeds are dispersed by the wind.

Kuss and his colleagues studied these populations of these species within the same geographic area using the same genetic analyses. They predicted that they would find a genetic structure of each species that correlated with the fragmented and isolated population structure, but that the species with the best long-distance dispersal adaptations would be less affected by isolation. Thus, populations of species with poor dispersal abilities that are widely separated would be less similar genetically. The scientists also predicted that small populations of each species would have a lower degree of genetic diversity.

Kuss and his colleagues extracted DNA and separated randomly selected segments of DNA using Random Amplification of Polymorphic DNA (RAPD). Multiple fragments of different sized DNA sequences results, and these can be separated by size on a gel. A particular pattern of bands of different sizes will be the result for each individual plant. Two plants that have the same exact genome will have the same pattern. Individuals who differ genetically will have different banding patterns, because DNA sequences would be of different size and would end up in different positions on the gels. In this way, RAPD can be used to analyze the genetic diversity of individuals and populations.

Figure 19.19 Heterozygosity and multiple alleles in Swiss Alp plant populations. A, The mean percentage of loci within a population that had multiple alleles, averaged over all populations. B, Heterozygosity, the percentage of heterozygous genes within a population, averaged over all genes, individuals, and populations. From Kuss et al., 2008, Table 2.

Genetic variation was measured as the number of unique band patterns and their frequencies within a population. In theory, a population that had zero genetic variation contained individuals that all had the exact same banding pattern. A population with more than one banding pattern has segments of DNA that have more than one allele. Kuss and his colleagues determined that there were 47 to 89 segments that had more than one allele, depending upon the species and determined the percentage of genes that had multiple alleles (Figure 19.19A).

From the frequency of bands of particular sizes, the scientists could also estimate which individuals were homozygous for particular segments and which individuals were heterozygous (that is, they had two alleles for the same DNA region). {Connections: Homozygous and heterozygous individuals are discussed in Sections 3.1 and 16.2. } Heterozygosity, the percentage of genes within a population that are heterozygous, varied across the species of plants, when averaged over all populations sampled (Figure 19.19B).

Kuss and his colleagues found no relationship between the size of populations and the two molecular diversity measures. Genetic distance, however, was correlated to geographical distance for all three species (Figure 19.20).

Figure 19.20 Genetic distances for each pair of populations with a best fit regression line through the data (solid line) and with 95% confidence interval for the best fit line (dotted lines). Each point represents the genetic and geographic distance (y and x, respectively) of a pair of populations. A, Alpine willowherb. B, Rose-like plant. C, Yellow bellflower. Figure 2 from Kuss et al., 2008, by permission of Botanical Society of China and Institute of Botany, Chinese Academy of Sciences, PRC.

The populations of these three species are fragmented, and this has led to population isolation due to distance between populations. The farther apart two populations are, the less likely they are to exchange genetic material by gene flow; and in this study, even populations close to each other were genetically dissimilar—to a degree. As geographic distance increased, so did genetic distance, which is indicated by the best fit lines in Figure 19.20. This effect was most pronounced for the rose (G. reptans). The mechanisms for dispersal, insects for pollen and wind for seeds, are unlikely to lead to long distance dispersal for this species. Wind may seem like it could carry seeds a long distance, but seeds may not be carried far in mountains with complex topography.

You observed a lot of variation in the data. Some pairs of populations that were geographically close had a high level of genetic distance, and others at the same distance had a low level, indicating perhaps that there was some randomness in the flow of genetic material—just by chance, pollen or seeds from one population may make it to another population regardless of distance. The average genetic diversity of these three mountain species was similar, and each had a low percentage of heterozygosity, which is predicted for isolated populations. Figure 19.19B indicates that up to 80% of genetic loci were estimated to be homozygous.

However, whether heterozygous or homozygous, there was a high percentage of DNA segments with multiple alleles within populations. This was contrary to the prediction of Kuss and his colleagues. If the populations are isolated, as they appear to be, then genetic diversity should decline over time as alleles are eliminated by chance events. Just by chance, individuals possessing a certain allele may not produce surviving offspring. Those chance events are part of the nature of genetic drift, which is a form of non-adaptive evolution. The fact that many alleles were not eliminated indicates that other evolutionary mechanisms may have been acting on the populations, or that not much time had passed since isolation occurred.

It was also surprising to the researchers that they did not find a correlation between measures of genetic diversity and population size. Genetic drift is often strongly affected by population size, because small populations are more likely, just by chance, to lose alleles that have low frequency in the population. In very small populations, or in species that have low dispersal abilities, loss of genetic diversity may be more extreme. For instance, orchids, plants you will study in Section 20.1 for their extremely high diversity, often exist in small, isolated populations. In that section, you will consider how, if natural selection acts differentially on isolated populations, speciation events could occur, given enough time. But if natural selection is not responsible for divergence of populations, you might conclude that non-adaptive evolution played a role, as Kuss and colleagues did.

A population bottleneck reduces genetic diversity

One consequence to a loss of genetic diversity in small populations is that species may not be able to adapt to changing environmental conditions, and this may be especially troublesome in light of global climate change. {Connections: Global climate change is discussed in Sections 11.2, 19.2, and 30.3.} It may also cause conservation of species to be more difficult, as endangered species are so-called because they have small population sizes. Another way that genetic drift may act on a population is through a sudden decrease in population size, called a population bottleneck.

Figure 19.21 Change over time in number of displaying black grouse cocks and the number of occupied breeding areas in the Netherlands from 1940 to 2007. From Larsson et al., 2008, Figure 1, © 2008 The Authors. Reprinted with permission from John Wiley and Sons.

Researchers led by Höglund studying the black grouse, Lyrurus tetrix, a European bird, documented a sharp decline in an isolated population living in the Netherlands. The population dropped to such a small size that only 9 to 32 displaying cocks (males) had been sighted from the mid-1980s to the mid-2000s, with only one breeding area from the mid-1990s onward. This was down from numbers in the thousands as recently as 1970 (Figure 19.21).

Figure 19.22 Heterozygosity and number of alleles in four black grouse populations. The museum population estimated values for the Dutch population in the past. Norway and Austrian populations were current but larger. Values are averages (with standard deviation error bars). From Larsson et al., 2008, Table 1

The Dutch population is over 200 kilometers away from other populations in Belgium and Germany, and the dispersal range for black grouse is typically no more than 30 kilometers for hens (females). Höglund and his colleagues examined tissue samples from museum specimens in the Netherlands and collected feathers and eggshells from living birds in the Dutch population and two other populations, one in Norway and one in Austria, that were much larger than the current Netherlands population and not isolated from other populations. The researchers extracted DNA and determined the genotypes and number of alleles for multiple genes. They calculated the proportion heterozygosity and the mean number of alleles per locus for each population (Figure 19.22), as well as the genetic distance among populations (Table 19.2). Bio-Math Exploration 19.4 explains how to interpret the confidence intervals in the table.

Table 19.2. Estimates of genetic distance among four populations of black grouse in Europe. Numbers in parentheses are the 95% confidence intervals for the estimates. From Larsson et al., 2008, Table 3, © 2008 The Authors. Reprinted with permission from John Wiley and Sons.

The sudden and dramatic decrease in population size of black grouse in the Netherlands led to significantly lower heterozygosity and numbers of alleles, on average, than determined for the ancestral population using the museum specimens. The sudden decrease in size, especially in the last decades of the 20th century, led to a loss of alleles. Those alleles may have been in low frequency, and individuals possessing those alleles may have randomly died or failed to breed, leading to their loss from the population. This is what would be expected when a population goes through a bottleneck. In addition, the low population size led to a higher probability of inbreeding as choice of mates is often limited to related individuals in a small isolated population. This further increased the relatedness among individuals and eroded genetic variation.

The comparison of the population at two different times was critical in piecing together the history of the Dutch black grouse population. The access to the museum population allowed Höglund and his colleagues to establish that the genetic structure changed as the population structure (size and distribution) changed. The ancestral Dutch population was similar, in terms of genetic variation, to the larger populations in Norway and Austria. Each was genetically less similar to the present Dutch population. This suggests that the Norwegian and Austrian populations have not changed genetically in the last 50 to 60 years, but that the Dutch population has. The three closely-related populations were large and genetically connected to other populations, which helped maintain genetic diversity.

Genetic drift, whether caused by isolation of small, fragmented populations or a population bottleneck, is an important mechanism of evolution and increases genetic distance between isolated populations of the same species. Drift, like natural selection, causes loss of genetic diversity over time. The mechanisms for loss of diversity are different as genetic drift leads to random, non-adaptive elimination of alleles from small, isolated populations. Given enough time and isolation, genetic drift and natural selection are powerful evolutionary forces that may result in speciation events. Time and isolation are important factors in both natural selection and genetic drift, whereas lack of isolation caused by gene flow may prevent speciation events.