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14 results for “Evolutionary trade-offs”
Data from: Macro-evolutionary trade-offs as the basis for the distribution of European bats
<p>We have compiled a dataset of life history traits and distribution characteristics of 30 European bat species, based on a literature study of a total of 56 primary and secondary sources. These life history traits are grouped into morphological, physiological and ecological adaptations. </p> <p><em>Physiological adaptations:</em></p> <p>Neonatal mass: the average weight (g) of a newborn pup, measured within five days after birth.</p> <p>Average litter size: the average size of a full-term litter (including stillborn pups) per female.</p> <p>Weaning mass: the weight (g) of a juvenile during its first flight outside the roost.</p> <p>Adult body mass: the average weight (g) of adult bats during the summer (between 1 May and 1 July), excluding pregnant females.</p> <p>Litter mass: neonatal mass * average litter size.</p> <p>Relative mass of neonatal to adult: neonatal mass*100 / adult weight</p> <p>Relative mass of litter to adult: litter mass*100 / adult weight</p> <p>Gestation: the length of gestation period (in days), from fertilisation to birth. When mated during autumn or winter, the sperm (or fertilised egg in <em>M. schreibersii</em>) is stored throughout the winter. On arousal from hibernation in the spring, around mid March, female bats ovulate and gestation begins. In accordance with other researchers (e.g. Altringham 1996, Entwisle <em>et al</em>. 1998), 15 March was used as the start of the gestation period, for statistical reasons we also included <em>M. schreibersii</em>.</p> <p>Weaning: the length of the lactation period (in days) until offspring are fully independent. After the juveniles are capable of flight, mothers continue to give their young nourishment until they are fully independent. Only when no extra nourishment is provided are the offspring considered fully weaned.</p> <p>Reproductive period: gestation + weaning (in days).</p> <p>Average age at first reproduction: The age (in days) at which 75% of the female population becomes sexual mature. Many species reproduce just before or during their first winter (at approximately 80 days old), but in some species the majority of the population postpone their sexual development. Individuals are stated to have become sexual mature if they participate in mating, have been found to be pregnant or inseminated.</p> <p>Observed average age: observed average age of adults in a population at a given time (in years).</p> <p>Longevity: the age (in years) of the oldest observed individual. The longevity can only be obtained by marking and later recapturing individuals. Most recapture data are collected in summer roosts or hibernacula. As not all species show the same fidelity to summer roost sites or can be found in hibernacula that are accessible to humans, this measure is sensitive to the chance of recapture.</p> <p>Minimum hibernation temperature: the minimum temperature (degrees Celsius) at which each species is observed.</p> <p> </p> <p><em>Morphological adaptations</em></p> <p>Length of forearm at birth: the length of the forearm (mm) of a newborn bat, measured between the elbow to the wrist of a folded wing. This is widely accepted as a measurement of size. Although it is not the best reflection of the length of an individual, it can be measured rapidly and accurately under field conditions.</p> <p>Length of forearm adult: The length of the forearm (mm) of an adult bat.</p> <p>Relative length of forearm of a newborn to an adult: (length of the forearm at birth*100)/ Length of forearm adult.</p> <p>Wing span: the length of the wings (m). The distance between the wingtips of a bat with wings extended so the leading edge is straight (including body width).</p> <p>Wing area: The combined area of the two wings (m<sup>2</sup>) including the entire tail membrane and the portion of the body between the wings.</p> <p>Wing loading: the relation between body weight, wing size and gravity (Nm<sup>-2</sup>). This measurement is related to the mean pressure on the wings. Wing loading is the weight (mass, in kg, times gravitational acceleration) divided by the wing area, i.e Wing loading = (weight adult*9.81)/ wing area. The wing load can vary significantly between geometrically similar bats. Because of such allometry, large bats have a higher wing load than smaller bats.</p> <p>Wing aspect ratio: the square of the wingspan divided by the wing area, i.e. Wing aspect ratio = (wingspan)<sup>2</sup> / wing area. This ratio can be interpreted as a measure of the aerodynamic efficiency of flight. A higher aspect ratio usually corresponds with greater aerodynamic efficiency (i.e. a streamlined body) and lower energy use in flight.</p> <p>Flight speed: The speed of flight (m/s). The speed of flight is usually measured in wind tunnel experiments or during radio-tracking.</p> <p> </p> <p><em>Ecological adaptations </em></p> <p>Maximum migration distance: the maximum observed distance (km) between the summer and winter habitat. In contrast to birds, the direction of migration in bats is not determined by the change of the seasons, but by the locations of the hibernacula. This migration distance can only be obtained by capturing, marking and later recapturing individuals. Bats often migrate across national boundaries and gathering recapture data requires international cooperation. The chance of recapture is sensitive to sample effort and local observation methods.</p> <p>Average migration distance: the average distance (km) between the summer and winter habitat. Most species of bats migrate both short and long distances. The same restrictions described for maximum migration distance also apply to this parameter.</p> <p>Echolocation type: the predominant echolocation type used by each bat species. European bats use one or sometimes a combination of the following four types of echolocations: fm-CF-fm, fm-QCF (with the QCF part dominant), FM-qcf (with the FM part dominant) and FM. For statistical reasons both FM-qcf and FM are clustered in the group FM. The FM-qcf and fm-QCF echolocations are both often loud and used to detect distant prey. FM and fm-CF-fm echolocations are softer and bats using these types of echolocation receive more detailed knowledge of their surroundings. Bats primarily use only one type of echolocation, although many can make some slight adjustments to this.</p> <p>Echolocation range: the maximum distance that an echolocating bat can detect a structure or object.</p> <p>Echolocation minimum frequency: the minimum echolocation frequency (MHz) used by each bat species.</p> <p>Echolocation maximum frequency: the maximum echolocation frequency (MHz) used by each bat species.</p> <p>Duration call (ms): the average duration (in ms) of one complete call cycle.</p> <p> </p> <p><em>Distribution parameters</em></p> <p>Northern limit of range: the most northerly observation (in latitude) of each bat species. This measurement includes anecdotal observations and observations of male bats.</p> <p>Northern limit of reproduction range: the most northerly observation (in latitude) of a maternity group. Note: confusion is possible between summer roosts and maternity roosts. Summer roosts are often inhabited by both males and females and less than 70% of the adult females participate in reproduction. Maternity roosts are predominantly occupied by females, and more than 70% of the adult females participate in reproduction.</p> <p>Southern limit of range: the most southerly observation (in latitude) of each bat species. This measurement includes anecdotal observations and observations of male bats.</p> <p>Southern limit of reproduction range: the most southerly observation (in latitude) of a maternity group. The same restrictions described for northern limit of reproduction range also apply to this parameter.</p> <p>Western limit of range: the most western observation (in longitude) of each bat species</p> <p>Eastern limit of range: the most eastern observation (in longitude) of each bat species</p> <p>Night length: The average night length (in hours) during midsummer (21<sup>st</sup> June) at the northern limit of the reproduction range.</p> <p> </p> <p>Sources: 1. Jones et al. 2009, 2. Krapp 2011, 3. Schober & Grimmberger 1997, 4. Norberg & Rayner 1987, 5. Hutterer et al. 2005, 6. Dietz et al. 2009, 7. Supplementary data from Barclay et al. 2004, 8. Wilkinson & South 2002, 9 Jones & Rydell 1994, 10. Norberg 1986, 11. Jones 1994, 12. Baagøe 1987, 13.Fleming & Eby 2003, 14. Neuweiler 2000, 15. Hayssen et al. 1993, 16. Kunz & Kurta 1987, 17. Russo & Jones 2002, 18. Brunet-Rossinni & Austad 2004, 19. Aldridge 1987, 20. Urbańczyk 1991, 21. Nagel & Nagel 1991, 22. Masing & Lutsar 2007, 23. Masing 1983, 24. Gaisler 1970, 25. Norberg 1987, 26. Baydemür & Albayrak 2006, 27. Dietz et al. 2006, 28. Sharifi 2004, 29. Kerth et al. 2001, 30. Schmidt 2005, 31. Smirnov et al. 2008, 32. Verbeek 1998, 33. Pandurkska & Beshkov 1998, 34. Harmata 1969, 35. Sachanowicz & Zub 2002, 36. Arlettaz et al. 2001, 36. Ibáñez et al. 2001, 37. Estók 2007, 38. Lohrl 1936, 39. Kunz & Hood 2000, 40. Happold & Happold 1990, 41. Rydell 1990, 42. Reiter 2004, 43. Ransome 1990, 44. Zahn 1999, 45. Deanesly & Warwick 1939, 46. Racey 1969, 47. Racey & Swift 1981, 48. Racey 1974, 49. Masing 1982, 50. Boyd & Stebbings 1989, 51. Lesiñski 1986, 52. Barak & Yom-tov 1991, 53. Arlettaz et al. 2000, 54. Gaisler et al. 1997, 55, Heise 1989, 56. Papadatou et al. 2009, 57. Unpublished data: own measurements.</p> <p> </p>
Data from: Evolutionary trade-offs of insecticide resistance – the fitness costs associated with target-site mutations in the nAChR of Drosophila melanogaster
<p>The evolution of resistance to drugs and pesticides poses a major threat to human health and food security. Neonicotinoids are highly effective insecticides used to control agricultural pests. They target the insect nicotinic acetylcholine receptor and mutations of the receptor that confer resistance have been slow to develop, with only one field-evolved mutation being reported to date. This is an arginine to threonine substitution at position 81 of the nAChR_β1 subunit in neonicotinoid resistant aphids. To validate the role of R81T in neonicotinoid resistance and to test whether it may confer any significant fitness costs to insects, CRISPR/Cas9 was used to introduce an analogous mutation in the genome of <i>Drosophila melanogaster</i>. Flies carrying R81T showed an increased tolerance (resistance) to neonicotinoid insecticides, accompanied by a significant reduction in fitness. In comparison, flies carrying a deletion of the whole nAChR_α<em>6</em> subunit, the target-site of spinosyns, showed an increased tolerance to this class of insecticides but presented almost no fitness deficits.</p>
Data from: Evolutionary trade-offs in the chemical defense of floral and fruit tissues across genus Cornus
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Data from: Evolutionary trade-offs of insecticide resistance – the fitness costs associated with target-site mutations in the nAChR of Drosophila melanogaster
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Evolutionary trade-offs may interact with physiological constraints to maintain color variation
<p>Animal coloration is a multifaceted trait with many ecological roles and related to a variety of developmental and physiological processes. Consequently, coloration is often subject to a variety of selective pressures, leading to the evolutionary maintenance of variation. In this study, we investigated hypotheses related to the maintenance of dorsal color variation in wood frogs (<i>Rana sylvatica</i>). First, we tested for multimodality, and whether color correlates with body size or condition or varies by sex or age-class. We combined behavioral trials with visual modeling to test for sex recognition. We also considered visual models for predators and tested for an interaction between discriminability indexes (JND) of color channel (chromatic vs. achromatic) and predator type (birds vs. snakes), as well as for a within individual trade-off between the JND of chromatic and achromatic coloration. Finally, we tested for disruptive viability selection on color using predation trials, and for antagonistic directional selection between viability selection and reproductive investment of females. We found that wood frogs present continuous color variation that does not correlate with body size or condition, but that changes with age. Wood frogs present subtle sexual dichromatism, but we found no evidence for a role of color in sex recognition. Instead, we discuss the possibility that sex differences might, at least in part, have a demographic explanation. Predator visual models indicated that wood frogs cannot solely rely on dorsal coloration for camouflage. Moreover, different predators might present selective pressures in different color channels, while individuals' achromatic and chromatic coloration trade-off in JND. Therefore, different selective pressures caused by different predators might interact with ontogenetic changes and developmental/physiological trade-offs to maintain color variation. We found no relationship between color and survival or reproductive investment, suggesting further work is required to fully understand selection on color. Our results highlight the importance of understanding evolutionary trade-offs and developmental/physiological constraints in combination with one another, and suggest the potential for an interaction between these proximate and ultimate mechanisms in the evolutionary maintenance of variation. These results likely extend beyond color expression in amphibians, and exemplify a more general process for such evolutionary outcomes.</p>
Evolutionary trade-offs may interact with physiological constraints to maintain color variation
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Data from: Is diversification in male reproductive traits driven by evolutionary trade-offs between weapons and nuptial gifts?
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Data from: Environment changes epistasis to alter trade-offs along alternative evolutionary paths
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Data for: Evolutionary rescue at different rates of environmental change is affected by trade-offs between short-term performance and long-term survival, by Martta Liukkonen, Ilkka Kronholm and Tarmo Ketola
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Data from: Parasites and competitors suppress bacterial pathogen synergistically due to evolutionary trade-offs
Parasites and competitors are important for regulating pathogen densities and subsequent disease dynamics. It is, however, unclear to what extent this is driven by ecological and evolutionary processes. Here we used experimental evolution to study the eco-evolutionary feedbacks between Ralstonia solanacearum bacterial pathogen, Ralstonia-specific phage parasite and Bacillus amyloliquefaciens competitor bacterium in the laboratory and plant rhizosphere. We found that while the phage had a small effect on pathogen densities on its own, it considerably increased the R. solanacearum sensitivity to antibiotics produced by B. amyloliquefaciens. Instead of density effects, this synergy was due to phage-driven increase in phage resistance that led to trade-off with the resistance to B. amyloliquefaciens antibiotics. While no evidence was found for pathogen resistance evolution to B. amyloliquefaciens antibiotics, the fitness cost of adaptation (reduced growth) was highest when the pathogen had evolved in the presence of both parasite and competitor. Qualitatively similar patterns were found between laboratory and greenhouse experiments even though the evolution of phage resistance was considerably attenuated in the tomato rhizosphere. These results suggest that evolutionary trade-offs can impose strong constraints on disease dynamics and that combining phages and antibiotic-producing bacteria could be an efficient way to control agricultural pathogens.
Data from: Sign epistasis limits evolutionary trade-offs at the confluence of single- and multi-carbon metabolism in Methylobacterium extorquens AM1
Adaptation of one set of traits is often accompanied by attenuation of traits important in other selective environments, leading to fitness trade-offs. The mechanisms that either promote or prevent the emergence of trade-offs remain largely unknown, and are difficult to discern in most systems. Here, we investigate the basis of trade-offs that emerged during experimental evolution of Methylobacterium extorquens AM1 to distinct growth substrates. After 1500 generations of adaptation to a multi-carbon substrate, succinate (S), many lineages had lost the ability to use one-carbon compounds such as methanol (M), generating a mixture of M+ and M− evolved phenotypes. We show that trade-offs in M− strains consistently arise via antagonistic pleiotropy through recurrent selection for loss-of-function mutations to ftfL (formate-tetrahydrofolate ligase), which improved growth on S while simultaneously eliminating growth on M. But if loss of FtfL was beneficial, why were M trade-offs not found in all populations? We discovered that eliminating FtfL was not universally beneficial on S, as it was neutral or even deleterious in certain evolved lineages that remained M+. This suggests that sign epistasis with earlier arising mutations prevented the emergence of mutations that drove trade-offs through antagonistic pleiotropy, limiting the evolution of metabolic specialists in some populations.
Data from: Parasites and competitors suppress bacterial pathogen synergistically due to evolutionary trade-offs
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Data from: Sign epistasis limits evolutionary trade-offs at the confluence of single- and multi-carbon metabolism in Methylobacterium extorquens AM1
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A multi-level understanding of the evolutionary trade-offs in thermal adaptation
GEO Series GSE140478. Escherichia coli K-12. 54 samples. Type: Expression profiling by high throughput sequencing.
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