BIO 101 UOP Population Genetics And Phenotypes Worksheet

BIO 101 UOP Population Genetics And Phenotypes Worksheet
A Sample Answer For the Assignment: BIO 101 UOP Population Genetics And Phenotypes Worksheet
Title:  BIO 101 UOP Population Genetics And Phenotypes Worksheet
BIO/101 Population Genetics 1. 2. 3. 4. 5. 6. 7. 8. Visit https://sepuplhs.org/high/sgi/teachers/evolution_act11_sim.html Click “Next” Select the following characteristics of your choice for Bird 1: Plumage, Body Size, Beak. Click “Next Bird” Repeat for Birds 2 and 3. Click “Next”. Read the text and Click “Continue”. Read the text and respond to the question: Of those that you selected, how fit do you think each phenotype is in the current environment? Click “Continue” Read the text and click “Close” Click “Start” The simulation will run and then stop at strategic intervals to tell you new information. Each time it stops, read the text, make any observations, and then click “Resume”. 13. Repeat until you reach the end of the simulation (500,000 years passed). 14. Click “Continue”. 15. Observe the information and read the text. Answer the following questions: 9. 10. 11. 12. Were your ideas about the fitness of each phenotype you selected correct? Explain. 16. If you would like, you may continue the simulation. However, you may now end the simulation if you choose. 17. Consider your readings from this week about Evolution and Population of species. How do your observations relate to the readings from this week? Copyright 2019 by University of Phoenix. All rights reserved. The Young, the Weak and the Sick: Evidence of Natural Selection by Predation Meritxell Genovart1*, Nieves Negre2, Giacomo Tavecchia1, Ana Bistuer3, Luı́s Parpal4, Daniel Oro1 1 Population Ecology Group, Department of Biodiversity and Conservation, IMEDEA (CSIC-UIB), Esporles, Spain, 2 Fundació Natura Parc, Santa Eugènia, Spain, 3 Servei de Gestió de Residus, Consell de Mallorca, Palma de Mallorca, Spain, 4 Consorci per a la Recuperació de la Fauna de les Illes Balears (COFIB), Santa Eugènia, Spain Abstract It is assumed that predators mainly prey on substandard individuals, but even though some studies partially support this idea, evidence with large sample sizes, exhaustive analysis of prey and robust analysis is lacking. We gathered data from a culling program of yellow-legged gulls killed by two methods: by the use of raptors or by shooting at random. We compared both data sets to assess whether birds of prey killed randomly or by relying on specific individual features of the prey. We carried out a meticulous post-mortem examination of individuals, and analysing multiple prey characteristics simultaneously we show that raptors did not hunt randomly, but rather preferentially predate on juveniles, sick gulls, and individuals with poor muscle condition. Strikingly, gulls with an unusually good muscle condition were also predated more than expected, supporting the mass-dependent predation risk theory. This article provides a reliable example of how natural selection may operate in the wild and proves that predators mainly prey on substandard individuals. Citation: Genovart M, Negre N, Tavecchia G, Bistuer A, Parpal L, et al. (2010) The Young, the Weak and the Sick: Evidence of Natural Selection by Predation. PLoS ONE 5(3): e9774. doi:10.1371/journal.pone.0009774 Editor: Adrian L. R. Thomas, University of Oxford, United Kingdom Received August 17, 2009; Accepted February 24, 2010; Published March 19, 2010 Copyright: ß 2010 Genovart et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: The authors acknowledge support mainly from TIRME, S.A. and also from EMAYA (Empresa Municipal de Agua y Alcantarillado) and the Spanish Ministry of Science (grant ref. CGL2006-04325/BOS). M.G. was supported by an I3P-CSIC fellowship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. * E-mail: m.genovart@uib.es many conservation agencies still control gulls by culling. The Local Government of the Balearic Islands (Spain) began a gull culling programme on a refuse tip in the island of Mallorca as a part of the population control of yellow-legged gulls -Larus michahellis- in the Balearic archipelago. From 2003 to 2007 birds were culled by two methods: by shooting or by the use of trained birds of prey (peregrine falcon -Falco peregrinus-, saker falcon -F. cherrug- and Harris’s hawk -Parabuteo unicinctus-). We gathered data from this culling program and examined killed birds to determine 1) the sex and age of the individual, 2) individual body condition, assessed from muscle condition, and 3) any sign of parasitism (internal and external), infection, malformation or chronic disease (e.g. aspergillosis). We used these data to investigate multiple prey traits simultaneously and to assess whether birds of prey killed randomly or by relying on specific individual prey features. Introduction Predation is an important selective force in evolution [1–7] and is generally assumed to select against substandard individuals, i.e. the young, senescent, sick, or individuals in poor physical condition [8–9]. Although some studies support this hypothesis and have contributed substantially to the understanding of selection by predation [10–20], (but see [18]), most of them are based on opportunistic observations or rely on some specific traits of the prey [15,18–20] or parasite load [12,13]. Kenward [10] and Temple [11] investigated morphological traits of individuals together with their healthy state, however, sample sizes were rather small (less than 30 prey) and the effects of different traits were not analysed simultaneously, so the contribution of each trait on differential predation it is difficult to evaluate. Additionally, all evidence typically comes from the typical predator-prey system, where traits may have coevolved in parallel, and thus predation upon substandard individuals could be an opportunistic foraging strategy rather than a response to substandard features of the prey. To fully understand the role of predation as a selective force, it is also necessary to collect evidence of predation outside the typical predator-prey system, gather information on large sample sizes, and investigate multiple traits of prey simultaneously. Populations of large gulls have increased substantially over the last century and some species are currently perceived as a pest by wildlife managers [21–24] but see [25]. As a consequence, many conservation agencies have set up culling programs to control gull populations, which typically consist of the systematic removal of large numbers of eggs, chicks or breeding adults. Even if the efficacy of these culling programs is still under debate [25–28], PLoS ONE | www.plosone.org Results We examined 506 gulls that had been shot and 122 gulls removed by raptors. The age structure in the shooting sample was similar to that observed at the dump over five available censuses (Breslow-Day homogeneity test of odds ratio, x 24 ~3.311, P = 0.507; Mantel-Haenszel, odds-ratio -log transformed- 95% Confidence Intervals: [20.066; 0.102], see Table 1). Post-mortem examination of gulls (Table 2) showed a low prevalence of external parasites; however, half of the individuals examined had internal parasites, mainly cestodes. About 7% of the gulls showed infection by Salmonella and 4% by Aspergillus. A few individuals also showed some kind of congenital malformation (bill deformity) and others had alteration of internal organs (Table 2), but prevalence of most 1 March 2010 | Volume 5 | Issue 3 | e9774 Natural Selection by Predation Table 1. Frequencies of gulls of each age-class (sub-adults and adults) counted during the censuses and compared with those of gulls shot at the dump. Table 2. Results of the exhaustive post-mortem examination of culled gulls (N = 506 and 122 for shooting and caught by raptors respectively). method Census no. 1 census age sub-adults adults Total 2 age sub-adults adults Total 3 age sub-adults adults Total 4 age sub-adults adults Total 5 age sub-adults Adults Total Veterinarian findings shooting Total 1045 224 821 31.1% 32.5% 496 1705 68.9% 67.5% 720 2526 3246 491 Shooting Raptors External parasites 2201 Lice 0.83 8.20 Ticks 0.14 0 Mites 0.56 0 Internal parasites 224 267 31.1% 28.9% 496 657 68.9% 71.1% Salmonella 7.36 1.64 720 924 1644 Aspergillosis 0.28 22.95 684 Retromandibular abscess 0 0.82 White spots on liver 0.14 0 White spots on intestinal wall 0 0.82 Lesions from mites 0.14 0 Atrophies 0 0.82 Lung granulomes 0 0.82 1153 224 460 31.1% 30.3% 496 1058 68.9% 69.7% 720 1518 2238 1342 224 1118 31.1% 29.0% 496 2737 68.9% 71.0% 720 3855 224 481 31.1% 32.8% 496 987 68.9% 67.2% 720 1468 Nematode 4.58 8.20 Cestode 47.08 43.44 Infections 1554 Internal organ findings 3233 Pericarditis 0.14 0 Pancreas congestion 0.14 0 4575 Hepatomegaly 0.56 0.82 705 Splenomegaly 7.22 5.74 Peritonitis 0.14 0 Airsacculitis 0.42 0.82 1483 Mechanical dysfunctions 2188 Frequencies and percentages of each age-class were shown separately for each census period. doi:10.1371/journal.pone.0009774.t001 Traumatism 1.53 33.61 Bill deformity 0.14 0.82 Fishing hooks 0 0.82 Arthritis 0.14 0.82 doi:10.1371/journal.pone.0009774.t002 veterinarian findings were low. Individuals that had been shot showed different characteristics than those killed by raptors (Table 3). The Multiple Component Analysis (MCA) scores plot also showed differences between groups, with more healthy adults and a higher average muscle condition within the group of individuals shot, and more juveniles, gulls in poor condition or showing some signs of illness in the group of individuals killed by birds of prey (Fig. 1). To test for the significance of these differences we used logistic regressions with predation by raptors as the response variable, being ‘‘killed by raptor’’ = 1 and ‘‘killed by shooting’’ = 0. In this way we retained shooting as the intercept of the regression to check for differential predation. The overdispersion value of the saturated model was 1.09, indicating a good fit of the data. The best ranked model (based on AIC values, Table S1) included a negative effect of age and a positive effect of both sickness and poor muscle condition on the probability of being predated by birds of prey (Table 4); this model did not include a gender effect. Strikingly, individuals in unusually good condition were also predated more frequently than expected by chance (Table 4). A model including an effect of sex explained the data equally well and was statistically equivalent to the previous model (Table S1) but the effect was not significant PLoS ONE | www.plosone.org Prevalence (z = 21.293, P = 0.196). Note that all models with lower AIC values unequivocally showed that age, muscle condition and sickness were clues for differential predation by birds of prey (see also Fig. 2). When these three variables were tested separately, results showed that muscle condition was the main factor affecting predation, this variable alone explaining 71% of the total variance. Discussion Natural selection of certain prey traits (e.g. morphological traits) has repeatedly been shown to be driven by predation [10–20]. Our paper could not address such particular issue, but on the other hand, the exhaustive analysis of a large number of prey, combined with the simultaneous analysis of a variety of traits, give us some general insights into how predation may operate in the wild. Here we show that predators did not kill individuals at random, but rather selected their prey on the basis of several, not always related traits. Our results indicated that age, muscle condition and sickness influence the probability of being predated, with juveniles, sick gulls, and individuals with poor muscle condition being killed 2 March 2010 | Volume 5 | Issue 3 | e9774 Natural Selection by Predation Our study also shows that not only individuals with severe diseases but also those with mild diseases are predated preferentially, indicating that subtle changes in behaviour or condition may have been sufficient to increase susceptibility to predation. This was also found by Miller et al (2000) who showed that prion infection in deer increased the rate of predation of deer by mountain lions (Puma concolor) nearly fourfold, even if few of the deer killed were recorded as ‘‘noticeably ill’’ by field observers prior to their deaths [31]. Our results clearly support the mass-dependent predation risk (MDPR) theory, which predicts that birds should keep their mass as low as possible to reduce their likelihood of being killed by predators [32,33]. To date, empirical evidence for this theory comes only from small passerines with body masses between 10–150gr [34,35 and references therein] and recently from one mammal [36]. Additionally, results showed preferential predation on those individuals with poor muscle condition, suggesting that stabilizing selection [37] could be operating on traits linked to body mass. This article provides a reliable, robust example of how natural selection by predation operates in the wild and strongly supports the paradigm that predators kill substandard individuals. Since gulls are an occasional prey of falcons and hawks, results probably indicate the ability of predators to detect substandard individuals in the wild rather than showing an optimal foraging strategy or possible coevolution within a natural predator-prey system. Table 3. Individual traits of gulls removed by shooting or by the use of birds of prey. Category Level Type of disposal Shooting (N) Age Sex Muscle condition Health Raptors (N) Total N Juveniles 9.68% (49) 36.88% (45) 94 2-year-olds 11.07% (56) 18.85% (23) 79 3-year-olds 13.83%(70) 4.09% (5) 75 Adults 65.41% (331) 40.16% (49) 380 Males 46.8% (237) 60.7% (74) 311 Females 53.2% (269) 39.3% (48) 317 Normal 89.9% (455) 37.7% (46) 501 Low 5.1% (26) 49.2% (60) 86 High 4.9% (25) 13.1% (16) 41 Good 78.7% (398) 50.0% (61) 459 Mild sickness 6.9% (35) 19.7% (24) 59 Severe sickness 14.4% (73) 30.3% (37) 110 506 122 628 Removed birds were examined to determine the sex and age of the individual, the individual nutritional state, assessed from fat layers and muscle condition, and any sign of infection, malformation or disease. We identified four age classes by plumage features: juveniles (from 0 to 1 year old), 2 years old (from 1 to 2 years old), 3 years old (from 2 to 3 years old), and adults (.3 years old). We determined three levels of body condition: normal, low and high, depending on the layers of muscle mass). Veterinarians determined if the illness detected was either severe or mild. doi:10.1371/journal.pone.0009774.t003 Materials and Methods From 2003 to 2007, we carried out 5 gull censuses at the landfill and estimated the proportion of birds in each age class. We identified four age classes by plumage features: juveniles (from 0 to 1 year old), 2-year-olds (from 1 to 2 years old), 3-year- olds (from 2 to 3 years old), and adults (.3 years old); these data were used to assess whether shooting was performed randomly regarding to age, by means of a goodness-of-fit test. We cannot exclude that shooting was biased in relation to veterinarian findings (health state) and muscular condition; however if a bias existed it would rend our comparison more conservative as individuals that were ill or in poorer condition should be shot preferentially [38]. Post-mortem examination of culled individuals was carried out at the Fundació Natura Parc-COFIB wildlife recovery centre in preferentially; thus strongly supporting the hypothesis that predators prey primarily on substandard individuals [8,9]. Natural selection acts on many characters simultaneously [29] and accordingly, our results dealing with natural selection by predation would suggest that two or more traits may affect fitness in an interactive way (i.e. correlational selection) [7,29,30]. Nevertheless, even if our sample sizes were relatively large, they still lack sufficient power to simultaneously test interactive effects between many characters. Figure 1. Multiple Correspondence Analysis between individuals shot and those killed by raptors. Map of the two main factorial axes from a Multiple Correspondence Analysis between individuals shot (noted by Shoot) and those killed by birds of prey (noted by Falco) depending on individual classification (F: Females; M: Males; J: Juveniles; I2: 2 years old; I3: 3 years old; A: Adults; H: Healthy individuals; S: Sick; AM: Abnormal muscle condition (low and high); NM: normal muscle condition). doi:10.1371/journal.pone.0009774.g001 PLoS ONE | www.plosone.org 3 March 2010 | Volume 5 | Issue 3 | e9774 Natural Selection by Predation number of gulls captured by falcons and those killed by shooting were not equally distributed throughout the year, with comparatively more gulls shot during the breeding period. As a consequence, we randomly balanced the sample size between both periods to assure the comparability of the two data sets. Individuals were shot at random (see above), with the same probability for all birds at the dump to be shot and shooting did not occur in a spatially segregated manner. Hence, comparison between the birds shot and those killed by falcons should reveal any predator preferences. We first used a MCA analysis including age, muscle condition, and sickness to visualize patterns of differentiation among gulls depending on the type of capture and the correlation among traits. We then used logistic regressions to test for differences between the two groups. We assessed the goodness-of-fit of the saturated model by estimating the overdispersion parameter (a value close to 1 indicated a good fit of the data). Model selection was based on Akaike’s Information Criterion (AIC); the model with the lowest AIC value was considered as the best compromise between model deviance and model parameters [39]. We also calculated the AIC weight as a measure of relative plausibility of each model. To avoid model over-parameterization, only additive models were considered and individual age was treated as a continuous covariate. All statistical analyses were done using the software R (www.r-project.org) and SPSS (version 16.0). Table 4. Estimates of the factors affecting predation from the best ranked model, which included age as a continuous covariate, muscle condition as a factor with three levels (normal, low and high), and sickness, as a factor with two levels (healthy and sick). Estimate Std. Error z value Pr(.|z|) Intercept 20.95 0.35 22.76 0.0058 Age 20.59 0.11 25.63 ,0.0001 Low 3.22 0.32 10.17 ,0.0001 High 1.79 0.40 4.46 ,0.0001 1.20 0.27 4.42 ,0.0001 Muscle condition Sickness doi:10.1371/journal.pone.0009774.t004 Mallorca. Within each age-by-sex class, we sorted individuals as healthy, mildly sick or severely sick depending on the veterinarian diagnosis on infections, mechanical or internal dysfunctions; individuals were also classified according to three levels of body condition depending on the pectoral musculature mass: normal, low or high (extremely large muscle mass). In subsequent analyses, sickness was treated in two ways, one by separating mild and severe sickness and a second one by grouping all sick individuals into a single group. Gulls breed in springtime and differences may exist in the number of individuals of different sex or age visiting the dump throughout the year, so we defined two periods: one encompassing the breeding season, from March to July, and a second one including the non-breeding period, from August to February. The Ethics Statement Animals were killed as a part of a culling program that the Local Government began for conservation issues as well as public health concerns. All animal work has been conducted according to relevant national and international guidelines and permits were Figure 2. Determinants of probability of being predated by the raptors. All juveniles and immature classes were grouped in a single, subadult age class and compared with adult gulls. Smoothing regression surfaces are represented using a Lowess method by iteration of weighted least squares on the selected variables. Highest probability of being killed by predators occurred on sub-adult gulls with severe sicknesses and abnormal muscle condition. doi:10.1371/journal.pone.0009774.g002 PLoS ONE | www.plosone.org 4 March 2010 | Volume 5 | Issue 3 | e9774 Natural Selection by Predation provided by Conselleria de Medi Ambient (Govern Balear). Authors were not responsible nor executed the culling programme. Acknowledgments We are grateful to the people involved in falconry and fieldwork, and to Deborah Bonner and Mike Fowler who corrected the English. Three anonymous referees and Erik Svensson provided helpful comments on previous drafts, and Lluı́s Jover helped with statistics. Supporting Information Table S1 Model selection of individual features of gulls predated by birds of prey compared to those shot at the landfill. Found at: doi:10.1371/journal.pone.0009774.s001 (0.04 MB DOC) Author Contributions Conceived and designed the experiments: MG NN GT AB LP DO. Performed the experiments: NN LP. Analyzed the data: MG. Wrote the paper: MG GT DO. Performed the veterinarian inspections: NN LP. References 1. Darwin CR (1859) On the origin of species. London: Murray. 432 p. 2. Ricklefs RE (1969) Natural Selection and the Development of Mortality Rates in Young Birds. Nature 223: 922–925. 3. Dawkins R, Krebs JR (1979) Arms races between and within species. Proc R Soc Lond Ser B Biol Sci 205: 489–511. 4. Endler JA (1986) Natural Selection in the Wild. Princeton: Princeton University Press. 321 p. 5. 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This is an open-access article distributed under the terms of the Creative Commons Attribution License: https://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. REVIEWS Natural selection and infectious disease in human populations Elinor K. Karlsson1,2, Dominic P. Kwiatkowski3,4 and Pardis C. Sabeti1,2,5 Abstract | The ancient biological ‘arms race’ between microbial pathogens and humans has shaped genetic variation in modern populations, and this has important implications for the growing field of medical genomics. As humans migrated throughout the world, populations encountered distinct pathogens, and natural selection increased the prevalence of alleles that are advantageous in the new ecosystems in both host and pathogens. This ancient history now influences human infectious disease susceptibility and microbiome homeostasis, and contributes to common diseases that show geographical disparities, such as autoimmune and metabolic disorders. Using new high-throughput technologies, analytical methods and expanding public data resources, the investigation of natural selection is leading to new insights into the function and dysfunction of human biology. Pathogens Viruses, bacteria or other microorganisms that can cause disease. Center for Systems Biology, Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts 02138, USA. 2 Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts 02142, USA. 3 Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK. 4 Wellcome Trust Sanger Centre for Human Genetics, Oxford OX3 7BN, UK. 5 Department of Immunology and Infectious Disease, Harvard School of Public Health, Boston, Massachusetts 02115, USA. Correspondence to E.K.K. and P.C.S. e-mails: elinor@broadinstitute.org; psabeti@oeb.harvard.edu doi:10.1038/nrg3734 Published online 29 April 2014 1 Infectious pathogens are arguably among the strongest selective forces that act on human populations1. Migrations and cultural changes during recent human evolutionary history (the past 100,000 years or so) exposed populations to dangerous pathogens as they colonized new environments, increased in population density and had closer contact with animal disease vectors, including both conventionally domesticated animals (for example, dogs, cattle, sheep, pigs and fowl) and those exploiting permanent human settlement (for example, rodents and sparrows)2,3. Consequently, both birth and mortality rates increased markedly 4. Host genetics strongly influences an individual’s susceptibility to infectious disease 5,6. Pathogens that diminish reproductive potential, either through death or poor health, drive selection on genetic variants that affect resistance; selection is likely to be most evident for pathogens with a long-standing relationship with Homo sapiens, including those that cause malaria, smallpox, cholera, tuberculosis and leprosy 7 (FIG. 1). We also contend with new threats, such as AIDS and severe acute respiratory syndrome (SARS). Some pathogens cause acute illnesses such as smallpox and cholera but, once survived, pose little additional threat. Other pathogens — for example, those causing malaria, tuberculosis and leprosy, as well as parasitic worms — can be carried as chronic infections and impair nutrition, growth, cognitive development and fertility. The timing, strength and direction (that is, positive, negative or balancing) of selection shape the patterns of variation that remain in the genome. These signatures of selection will therefore vary with the age, geographical spread and virulence of the pathogen. For those with access, modern medicine radically diminishes exposure to various pathogens. In developed countries, vaccination, better nutrition and improved public health have eliminated diseases that were common in the past 8. Common immune-mediated diseases may be partly caused by evolutionary adaptations for resistance and symbiosis with potentially dangerous microorganisms9–12. For example, decreased gut microbiome diversity in residents of developed countries13 may alter mucosal immune responses14. Understanding host– pathogen interactions will inform the development of new therapies both to counter ongoing pathogen evolution and to better manage immune-mediated diseases15. Here, we review how the technological revolution in genomics allows us to examine human adaptation to infectious disease in new ways. Natural selection leaves distinctive signatures in the genome, as genetic variants that improve survival and reproduction increase in frequency, and detrimental variants vanish. Hundreds of candidate regions of selection were identified in early genomic data sets, but only few adaptive variants were identified16. High-throughput biotechnology enables large-scale surveys of genome diversity,