Properties of nuclear emission.
\\n\\n
Dr. Pletser’s experience includes 30 years of working with the European Space Agency as a Senior Physicist/Engineer and coordinating their parabolic flight campaigns, and he is the Guinness World Record holder for the most number of aircraft flown (12) in parabolas, personally logging more than 7,300 parabolas.
\\n\\nSeeing the 5,000th book published makes us at the same time proud, happy, humble, and grateful. This is a great opportunity to stop and celebrate what we have done so far, but is also an opportunity to engage even more, grow, and succeed. It wouldn't be possible to get here without the synergy of team members’ hard work and authors and editors who devote time and their expertise into Open Access book publishing with us.
\\n\\nOver these years, we have gone from pioneering the scientific Open Access book publishing field to being the world’s largest Open Access book publisher. Nonetheless, our vision has remained the same: to meet the challenges of making relevant knowledge available to the worldwide community under the Open Access model.
\\n\\nWe are excited about the present, and we look forward to sharing many more successes in the future.
\\n\\nThank you all for being part of the journey. 5,000 times thank you!
\\n\\nNow with 5,000 titles available Open Access, which one will you read next?
\\n\\nRead, share and download for free: https://www.intechopen.com/books
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
Preparation of Space Experiments edited by international leading expert Dr. Vladimir Pletser, Director of Space Training Operations at Blue Abyss is the 5,000th Open Access book published by IntechOpen and our milestone publication!
\n\n"This book presents some of the current trends in space microgravity research. The eleven chapters introduce various facets of space research in physical sciences, human physiology and technology developed using the microgravity environment not only to improve our fundamental understanding in these domains but also to adapt this new knowledge for application on earth." says the editor. Listen what else Dr. Pletser has to say...
\n\n\n\nDr. Pletser’s experience includes 30 years of working with the European Space Agency as a Senior Physicist/Engineer and coordinating their parabolic flight campaigns, and he is the Guinness World Record holder for the most number of aircraft flown (12) in parabolas, personally logging more than 7,300 parabolas.
\n\nSeeing the 5,000th book published makes us at the same time proud, happy, humble, and grateful. This is a great opportunity to stop and celebrate what we have done so far, but is also an opportunity to engage even more, grow, and succeed. It wouldn't be possible to get here without the synergy of team members’ hard work and authors and editors who devote time and their expertise into Open Access book publishing with us.
\n\nOver these years, we have gone from pioneering the scientific Open Access book publishing field to being the world’s largest Open Access book publisher. Nonetheless, our vision has remained the same: to meet the challenges of making relevant knowledge available to the worldwide community under the Open Access model.
\n\nWe are excited about the present, and we look forward to sharing many more successes in the future.
\n\nThank you all for being part of the journey. 5,000 times thank you!
\n\nNow with 5,000 titles available Open Access, which one will you read next?
\n\nRead, share and download for free: https://www.intechopen.com/books
\n\n\n\n
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VVER −1000 (Water-Water Energetic Reactor-1000) is a type of pressurized water reactor with 1000 MW thermal power planned to generate a 330 MWe [1]. Production actinide consequently of using nuclear power reactors as electric energy source. Actinide Inventory (AI) elements cumulative in spent fuel (SF) and are a part of spent fuel that useable as MOX fuel in nuclear power reactors. Recently, some researchers have been studied the actinide inventory in spent fuel of nuclear power reactors [2, 3, 4].
VISTA computer code is available for the calculation of nuclide inventories in spent fuel. The neutron transmutation (fission) of the long-lived actinide isotopes in SF with decay times on the order of millennia into fission products with decay times of a few hundred years would profoundly impact the problem of storing SF that confronts the expansion of nuclear power. For the actinides, the creation comprises of neutron catch or decay of a forerunner nuclide. Evacuation may comprise of neutron-actuated or unconstrained fission; neutron catch and radioactive decay [5].
The estimation of the response rates requires nuclide fixation and cross-area information, the neutron transition level and vitality range in the fuel. As the energy spectrum in the fuel is subject to the grid structure and arrangement, such counts include rehashed iterative answers for the range and cross-section. The degree to which this is completed relies upon the precision expected of the last arrangement. After every burnup span, the combined range is utilized to get the neutron cross-segments which are accordingly utilized for the count of the nuclide response rates. The focuses to be considered in making an assessment of the accessible strategies are:
data of nuclide cross-section
energy spectrum evaluation
neutron flux level calculation during the irradiation
burnup equations numerical solution.
The treatment of these amounts in the few elective codes has been analyzed [6].
Nuclear fuel cycle definition is the set of cycles to utilize nuclear materials and to restore it to conclusive state. The fuel cycle begins with the mining of unused atomic materials from nature and closures with the protected removal of utilized nuclear materials in nature. Figure 1 shows the nuclear fuel cycle diagram by indicating main processes in a recycle mode.
The nuclear fuel cycle diagram.
The first step is mining in a nuclear fuel cycle. After this step the next step is milling prosses. The feed for mining and processing measure is U metal and the item is U3O8 concentrate, which is generally called yellowcake because of its shading and shape [7]. The third step is change term that alludes to the way toward purging the U concentrate and changing over it to the synthetic structure required for the following phase of the nuclear fuel cycle.
In this stage U element can be produced in three forms of metal, oxide (UO2 or UO3) and uranium hexafluoride (UF6). UF6 is the overwhelming item at this phase of the nuclear fuel cycle since it is handily changed over to gas for the advancement stage, as utilized on the planet’s most regular reactor type. (LWRs) (see Figure 2).
Main components of a light water reactors (LWR) [
The next process after conversion is enrichment step. In general, there are two industrially accessible advancement innovations: vaporous dispersion and rotator. The two strategies depend on the slight mass contrast somewhere in the range of 235U and 238U. Along these lines, the improvement is characterized as the way toward expanding the measure of 235U contained in a unit amount of uranium. The feed for this stage is regular UF6 and the item is enhanced UF6. The other yield of the cycle is the uranium which has lower 235U substance than the regular uranium. It is known as enhancement tail or exhausted uranium. Fuel fabrication is another term that the enrichment fuel was made as pellets. Fuel pellets are loaded into tubes of zircaloy or stainless steel, which are sealed at both ends. These fuel rods are spaced in fixed parallel arrays to form the reactor fuel assemblies (see Figure 3).
The fuel fabrication [
The whole process is referred as fuel fabrication. The reactor unit itself is irradiator for nuclear fuel. It burns the fuel, produces energy and spent fuel. The feed for reactor is new fuel containing U or U/Pu, if there should arise an occurrence of blended oxide (MOX) fuel, for existing atomic fuel cycle alternatives. The item is the spent fuel comprising of recently created nuclides, for example, splitting items (I. Cs, Sr, …) minor actinides (Np, Am, Cm) and Pu just as the uranium. The greatest aspect of the spent fuel is still U (over 95% for the most reactor types). Reprocessing process is based on chemical and physical processes to separate the required material from spent nuclear fuel. The feed of this process is spent fuel and the products are reusable material and high-level wastes (HLW) [6].
The other unit of nuclear cycle fuel is spent fuel storage, which could be put away briefly for some time later or could be put away uncertainly. Spent fuel could be put away in pools (wet sort, briefly) or in storehouses (dry sort). Likewise, the loss from fuel manufacture and reprocessing offices are delegated HLW and requires cautious treating. HLW is put away in uncommon storerooms after legitimate treatment.
The following nuclides have been studied and the transmutation chain which is given in Figure 4. These radionuclides are: 235U, 236U, 238U, 238Pu, 239Pu, 240Pu, 241Pu, 242Pu, 237Np, 241Am, 242mAm, 243Am, 242Cm and244Cm.
The actinide transmutation chains [
The actinide transmutations to each chine are calculated by [10]:
where
If the neutron flux and cross sections are constant on a time interval, the equation has a simple analytical solution.
An example to solve the transmutation chain starting from 238U up to 240Pu is shown below, using Bateman’s Equation.
where.
AFi = Isotope(i) atomic content in the chain
σ c = Cross-section of capture (barns)
σ f = Cross-section fission (barns)
σ n,2n = Cross-section of (n,2n) (barns)
σ ex = Cross-section of excited (barns)
σ t = Cross-section totally (barns)
T 1/2 = Half-life (years)
Φ = Neutron average flux (n/cm/cm/sec). (the energy range of 0 to 10 MeV total flux)
T = Time of irradiation (sec)
Ed = Burnup discharge (GW·d/t)
KWKG = Specific power (MW/tonne)
The condition solver initially computes the isotopic piece in nuclear division. The acquired nuclear portions at that point are changed over to the weight divisions [6].
Nowadays, it is estimated that >2000 t of actinides has been accumulated as nuclear waste, most of which are plutonium isotopes. Table 1 shows the composition of transuranic elements in the fresh and spent fuel of a VVER after recycling process [10]. The most significant commitment to the drawn-out radiation peril originates from 239Pu (t½ = 24,110 a), from other Pu isotopes, and from other actinides, i.e., 237Np (t½ = 2.1 × 106 a), 241Am (t½ = 432 a), 243Am (t½ = 7370 a) and 245Cm (t½ = 8500 a) [11]. Pu and MA represent only 1.5% of the waste volume. Nonetheless, their radio toxicity becomes dominant after around 300 years and remains extensively high for a huge number of years, a period too long to even consider guaranteeing a sheltered disengagement from nature by methods for building obstructions [12]. Besides, actinides present criticality and multiplication concerns. The fission cross-section of numerous actinides is portrayed by edges of a couple of 100 keV. Hence, they do not undergo fission in thermal reactors, rather reduce reactor critically as thermal neutron absorbers. However, they have significantly high fission cross-sections at high neutron energies [13].
Radiation | Mass (u) | Charge | Range (air) | Range (tissue) |
---|---|---|---|---|
α | 4 | +2 | ~3 cm | ~40 μm |
β | -1 or + 1 | ~300 cm | ~5000 μm | |
X or gamma emission | 0 | 0 | Very large | Through body |
Fast neutron (n) | 1 | 0 | Very large | Through body |
Thermal neutron (n) | 1 | 0 | Very large | ~15 cm |
Properties of nuclear emission.
The amount of nuclear materials for a VVER-1000 reactor was calculated and shown as diagram in Figure 5.
The flowchart of nuclear material amounts calculated by VISTA.
For VVER-1000 reactor, the fresh fuel, actinide elements and fission product values in spent fuel was calculated by VISTA simulation code.
The total amount of FF is 23.792 t/year with 22.915 t/year of 238U and 0.877 t/year of 235U. The grade of enrichment is 3.6% on average. The actinide martials content in SF of calculated by VISTA are 235U (0.232123 t/year), 236U (0.107850 t/year), 238U (22.177277 t/year), 238Pu(0.004352 t/year), 239Pu(0.156181 t/year), 240Pu(0.047959 t/year), 241Pu(0.049525 t/year), 242Pu(0.017008 t/year), 241Am(0.001297 t/year), 237Np(0.001239 t/year), 242m Am(0.000019 t/year), 243Am(0.003554 t/year), 242Cm(0.000463 t/year) and 244Cm(0.001142 t/year) radioelements. The values of above radioelements except 235U and 238U isotopes were compared in Figure 6.
The actinide elements content in spent fuel of the VVER-1000 reactor calculated by VISTA.
Also, the content of discharged UOX burned fuel in VVER-1000 nuclear power plant is presented in Figure 7.
Discharged UOX spent fuel content in VVER-1000 reactor.
There are two type radiation sources naturally occurring radioactive materials (NORM) and technologically enhanced naturally occurring radioactive materials (TENORM) consist of materials in nuclear industry. The NORM radionuclides like 232Th, 238U, and 40K that occur mostly in minerals such present all over the Earth’s crust in varying quantities depending on the ambient geological end geochemical properties of local. NORM radioactive are present in soil [14, 15, 16, 17, 18, 19], water [20, 21, 22, 23] and building materials [24, 25, 26, 27, 28, 29, 30]. The TENORM materials is upset or changed from regular settings or present in a mechanically improved state due to past or introduce human exercises and practices, which may bring about a relative increment in radionuclide fixations, radiation presentations and dangers to people in general, and danger to the open condition above foundation radiation levels.
The properties and ranges of the various nuclear radiations are summarized in Table 1. The ranges are only approximate since they depend on the energy of the radiation [31].
The alpha particle has mass higher than beta particle, so these partials travels relatively slowly into matter. Alpha particle interaction is a high likelihood of with iotas along its way and will surrender a portion of its vitality during every one of these cooperation’s. As an outcome, α particles lose their vitality quickly and travel without a doubt, extremely short separations in thick media.
Beta particles are a lot of littler than particles and travel a lot quicker. They consequently go through less associations per unit length of track and surrender their vitality more gradually than α particles. This implies β particles travel further in thick media than α particles.
Gamma radiation loses its vitality mostly by interfacing with nuclear electrons. It ventures enormous separations even in thick media and is hard to ingest totally.
Neutrons surrender their vitality through an assortment of collaborations, the general significance of which are reliant on the neutron vitality. Therefore, it is regular practice to separate neutrons into in any event three vitality gatherings: quick, moderate and warm. Neutrons are infiltrating and will travel enormous separations even in thick media.
An office ought to have set up a radiation assurance program that is satisfactory to secure the radiological wellbeing and wellbeing of laborers and the general population and guarantee that the presentations are ALARA. To achieve this, offices assess and describe the radiological hazard and regularly give adequate hearty controls to limit this danger. Potential mishap arrangements are considered in evaluating the ampleness of the controls, which expect to limit radiological danger and sullying.
The fuel cycle office radiation assurance rehearses incorporate [32]:
A viable reported program to guarantee that word related radiological introductions are ALARA;
An association with sufficient capability prerequisites for the radiation insurance work force;
Approved composed techniques for directing exercises including radioactive materials;
Radiation protection preparing for all faculty who approach limited zones;
A program to control airborne convergence of radioactive material with building controls and respiratory insurance;
A radiation overview and checking program that incorporates prerequisites for control of radioactive sullying inside the office and observing of outside and inward radiation presentations;
Other projects to look after records, to report radiation introductions to the managing authority, and to restore an adequate in-plant radiological condition in case of an occurrence.
The execution of such projects with respect to coordinate radiation is currently made a lot simpler with the utilization of individual electronic dosimeters of Visa size that can immediately alarm the holder when momentary or cumulated portion reach modified edges, that keep in memory the historical backdrop of presentation and whose information can be downloaded to PCs, for instance each time the administrator enters or leaves the controlled zone, so these information can be naturally recorded and investigated. In this manner, point by point presentation previsions can be checked versus real introductions, permitting improvement of both working techniques and previsions. The improvement of mechanized screens that permit the perception of portion rates is likewise an incredible asset for radiation protection.
The content of this chapter is overall reviewing the nuclear fuel transmutation discussion. For this purpose, the nuclear fuel cycle of UOx type fuel was presented. In the next section the composition of transuranic in the spent fuel of VVER reactor was survived. Also, the amount of minor actinide and fission product in a VVER-1000 reactor was calculated and finally, the radiation protection principles of nuclear fuel cycle were presented and discussed.
The author would like to appreciate from to H. Tulsidas, Division of Nuclear Fuel Cycle and Waste Technology of IAEA.
The authors declare no conflict of interest.
Genomic signatures associated with species, genepools, and ecotypes’ divergence can result from causes other than reduced gene flow, for example, random genetic drift and selection [1]. Moreover, the origin of the outlier variants from novel or standing genetic variation leads to distinctively different patterns of genomic divergence [2, 3, 4]. One approach that can help to distinguish these underlying causes of divergence is carrying out a replicated sampling of contrasting populations [5, 6]. If genetic drift rather than selection is responsible for the divergence, it is unlikely that signals of differentiation reappear consistently across replicates [5]. On the other hand, if selection acted on the same genetic variants at the replicated contrasting pairs, genomic regions with comparatively high divergence between individuals from contrasting populations should be identical at each of the replicated populations. Parallel selection on shared genetic variation should therefore lead to low divergence within populations and across replicates, in the exact genomic regions where equivalent variants are selected at each contrasting population [6]. Discerning among gene flow, genetic drift and selection as the cause of parallel genomic divergence are possible as long as there is some degree of replication considered in the sampling of contrasting populations.
\nThe genomic landscape of divergence can also be influenced by differences in ancestral variation and recombination in the genome [7, 8]. Lineage sorting may be enhanced relative to background levels by a reduction in the effective population size (Ne) due to processes other than gene flow, like low recombination [8, 9, 10]. Since differentiation is further speeded up in low-recombining regions because of linked selection [11, 12, 13], the imprint caused by genomic features on the differentiation landscape should be ubiquitous across different levels of divergence. Therefore, besides a replicated sampling of contrasting populations, a hierarchical nested sampling across various scales of divergence is advisable in order to examine whether genomic islands of divergence may display differentiation due to suppressed recombination, smaller effective population size, and increased drift.
\nIn order to discern among confounding causes of genomic divergence in a system with strong population structure and subjected to domestication, we suggest conducting the following analyses by taking advantage of a replicated hierarchical nested sampling across various scales of divergence:
Analyze whether FST outliers between species coincide with high FST values at within-species comparisons. This pattern is expected if genomic islands of speciation are repeatedly more prone to harbor within-species divergence as a result of limited recombination [8].
Assess whether the within-species between-genepool divergence FST profiles are similar among four available comparisons. This trend is expected if the same variants were selected as the result of similar selective pressures at multiple domestication events, but not if divergence outliers were due to population divergence, that is, genetic drift [5].
Assess whether the within-genepool wild-cultivated divergence FST profiles are similar among the available comparisons. This coincidence is expected if the same variants are selected as the result of parallel domestication but not if divergence is due to genetic drift [5].
Finally, we suggest exploring if regions of high FST co-localized with regions of low FST in within-population comparisons. ΔDiv can be used to analyze the difference between these two FST values in each window. Peaks in the ΔDiv statistic point to genomic regions that diverged as a result of parallel divergence from shared variation rather than due to novel variation evolving at each site [6].
\nWith this in mind, in this chapter we discuss how the recurrent phylogeographic splits and nested domestication events of common and lima beans help understand whether genomic islands of speciation in
Are between-species FST outliers recovered in within-species comparisons?
Is there any parallelism in the within-species divergence FST profiles?
Are low-recombining regions (i.e., centromeres) more prone to exhibit divergence across repeated and hierarchically nested scales of divergence?
If there were some parallelisms in the genetic adaptations to the Mesoamerican and Andean environments or in the genetic consequences of the domestication syndromes, then there would be matching signals of differentiation in the within-species between-genepool divergence FST profiles and in the within-genepool wild-cultivated divergence FST profiles, respectively. These patterns of repeatability would not be observed if between-genepool and wild-cultivated divergence outliers were due to genetic drift [5], if selection pressures were different [22] or if equivalent selective forces did not act on the same shared variation [6, 23]. Yet, genomic constrains, rather than true signals of convergent adaptation and domestication, could still be the reason for these parallelisms. If genomic features were indeed constraining divergence, then genomic islands of differentiation would coincide with low-recombining regions regardless the nature and the scale of divergence.
\nBy looking at the genomic diversity patterns in common and lima beans [24, 25, 26, 27, 28, 29, 30], there is evidence that differentiation across repeated and hierarchically nested levels of divergence always co-occurs with regions of low SNP density (Figure 2). Increased lineage sorting, and consequently rapid differentiation, is a common phenomenon in low-recombining regions because of linked selection and a reduction in the effective population size [8, 9, 10]. Likewise, low-recombining regions also tend to exhibit a decline in diversity due to background selection and, to a lower extent, because of genetic hitchhiking [11]. This can be understood as evidence that regions with low SNP diversity are enriched for contiguous signatures of differentiation between bean species, between genepools, and as part of the multiple domestication syndromes. These concurring signatures could be a by-product of genomic constrains inherent to low-recombining regions.
\nSchematic representation of a sampling across hierarchically nested sampling levels of divergence.
One of the regions that repeatedly exhibit high differentiation across hierarchically nested levels of divergence in the presence of low SNP density is the centromeric section of chromosome Pv11. The wild-cultivated divergence peak in this chromosome is shared by three domestication syndromes and is located beside the outlier peak detected for all within-species between-genepool comparisons, which in turn coincides with a major between-species peak. In this wide section of chromosome Pv11, there are indications that convergent divergence is consistently correlated with very low SNP density, as expected because of combined effects of linked and background selection in low-recombining regions [8, 9, 10, 22]. The observation that genomic constrains are biasing divergence across scales in this section of chromosome Pv11 is reinforced by the fact that previous genomic scans did not attribute to this region a consisted outstanding role during the domestication syndromes [20, 21] or in conferring adaptation to different environments and latitudes across the Americas [31]. The only exception is the candidate gene influencing plant size (
Patterns of genome-wide diversity in common bean and lima beans based on 13,213 GBS-derived SNP markers. A sliding window analysis (window size = 1 × 107 bp, step size = 500 kb) was used to compute (
Other “hotspots” for spurious divergence due to genomic constrains may be the regions with low SNP density in chromosomes Pv8 and Pv10 that exhibit signatures of between-species divergence as well as repeated between-genepool and within-genepool wild-cultivated divergence (Figure 2). The region in chromosome Pv8 was previously reported to be highly divergent during the domestication of the Andean common bean, but then there were not candidate genes in this region associated with that domestication syndrome in particular [20], despite that the same region is known for being involved in plant and seed growth (i.e.,
The observation that low-recombining regions are enriched for differentiation across repeated and hierarchically nested levels of divergence in
There is some evidence of some parallelisms in the genetic adaptations to the Mesoamerican and Andean environments in common and lima beans (Figure 2). The landscape of genomic adaptation has remained largely unexplored in
The genomic consequences of multiple domestication events are also moderately recurrent as revealed by our survey. From the twelve regions putatively differentiated as the result of the domestication syndrome, only five (42%) appear in more than one comparison but none appears in all. Two peaks in chromosome Pv3 and Pv10 are repeated across three different comparisons of all five profiles of the domestication syndromes. At least the region in chromosome Pv3 has been reported to be involved in the vernalization pathway (i.e.,
Also striking is the rarity of regions putatively involved in domestication and shared by several domestication events. This trend, mostly expected for quantitative traits with complex genetic architectures [59, 60, 61], had already been noticed for the common bean [20]—potentially applying for lima bean as well [21], and so does not necessarily speak for a prevalent role of drift. Since divergence in the lack of repeatability is a liable result of lineage sorting, caution must be undertaken while interpreting these signals. Singularities may result from different adaptive pressures across the Americas unique to each species, distinctive adaptation to the Mesoamerican microenvironments, dissimilar selection as part of each domestication event [22], equivalent selective forces acting on different genetic variants [6, 23], or genetic drift [5]. Discerning among these causes requires further genotyping in an extended panel specifically addressing each comparison. At least for the divergence peak at chromosome Pv7 in the wild-cultivated Mesoamerican common bean comparison, other drivers besides the domestication itself are an unlikely reason for divergence because a wide region in chromosome Pv7 region is known for being associated with increased seed weight (i.e.,
Genomic islands of speciation are not necessarily more prone to harbor within-species divergence, yet subjacent genomic constrains could still be shaping parallel divergence at broader genomic scales. With that in mind, we first discussed how genomic features and linked selection could enhance convergent differentiation in low-recombining regions. Later, we reviewed cases of moderate repeatability in the genomic consequences of multiple adaptation and domestication events. This chapter emphasizes that differentiation across repeated and hierarchically nested levels of divergence co-occurs with regions of low SNP density, and these concurring signatures may be a by-product of genomic constrains inherent to low-recombining regions. We advise a more systematic use of repeated and hierarchically nested samplings in order to improve our understanding of the underlying causes of the genomic landscape of divergence. Because certain regions are more prone to accumulate islands of divergence as the result of genomic constrains, we advocate that studies of genomic divergence should consider more systematically a dual-purpose sampling, such as the one we described in the first section. In the first place, using replicated populations under presumably similar selection pressures helps accounting for lineage sorting and characterizing the nature of the selected variants, i.e., novel versus standing [6]. Second, a hierarchically nested sampling across various levels of divergence allows for further assessments on the processes, which like genomic constrains, may give rise to parallel divergence patterns [2, 3, 4, 62]. Finally, some of these examinations must be verified with genomic features and estimates of the recombination rate [63, 64, 65]. We foresee that as the evidence of pervasive genomic constrains shaping genomic differentiation across species and at countless scales of divergence accumulates, replicated samplings of contrasting populations in a hierarchically nested framework of divergence will become indispensable.
\nIn the long run, we are looking forward to see more coherent and systematic samplings of replicated contrasting populations across hierarchically nested levels of divergence in of genomic divergence has always been challenging, but the field is now moving forward toward a more cohesive framework. New ways [66, 67] to characterize obscuring genomic features promise aiding our understanding on how the genomic landscape of divergence is shaped.
\nAmong the five domesticated species in the
Some of the ideas presented in this chapter were refined thanks to the comments from A. Caro, I. Cerón, C. Jiggins, D. Londoño, P. Reyes, C. Salazar, J.J. Wiens, and R. Yockteng during the VI Symposium of the Colombian Society for Evolution held in Cali (Colombia) on August 2017. This chapter was funded by a Colciencias (Colombia) grant awarded to MC under contract number FP44842-009-2015 and project code 1101-658- 42502, by the grant 3404 from Fundación para la Promoción de la Investigación y la Tecnología del Banco de la República de Colombia to MC, and by the Lundell and Tullberg (Sweden) grants to AC. The Geneco mobility fund from Lund University is thanked for subsidizing the meeting between AC and MB in the spring of 2015 at Nashville (TN, USA). AC’s writing time was sponsored by the grants 4.1-2016-00418 from Vetenskapsrådet (VR) and BS2017-0036 from Kungliga Vetenskapsakademien (KVA). MB received support from the Evans-Allen fund of the US Department of Agriculture. The editorial fund from the Colombian Corporation for Agricultural Research is acknowledged for financing this publication.
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