\r\n\tThe emergence of novel prion strains in animals, which include the only evidenced zoonotic prion C-BSE causing vCJD in humans, has created an important public health concern. Currently, new threats to human and animals may develop because of the plausible zoonotic properties of scrapie, L-BSE and the recently emerging chronic wasting disease in Europe. \r\n\tThis book will gather experts in prion diseases and present new scientific advances in the field and relations with other amyloid neuropathologies.
",isbn:null,printIsbn:"979-953-307-X-X",pdfIsbn:null,doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"b1a3eb035e9f5baee9320a5e0d1ec07c",bookSignature:"Dr. Yannick Bailly and Dr. Benoit Schneider",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/7984.jpg",keywords:"Synapse, Stress, Myelin Maintenance, PrPc Loss of Function, Apoptosis, Chronic Wasting Disease, CWD, Prion Transmission, Scrapie, Cross-Species Barrier, Alzheimer's Disease, Prionoid",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"June 5th 2019",dateEndSecondStepPublish:"October 1st 2019",dateEndThirdStepPublish:"November 30th 2019",dateEndFourthStepPublish:"February 18th 2020",dateEndFifthStepPublish:"April 18th 2020",remainingDaysToSecondStep:"a year",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:null,coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"164577",title:"Dr.",name:"Yannick",middleName:null,surname:"Bailly",slug:"yannick-bailly",fullName:"Yannick Bailly",profilePictureURL:"https://mts.intechopen.com/storage/users/164577/images/system/164577.jfif",biography:"Yannick Bailly was born in 1956. During his Ph.D. at the University of Strasbourg and the University of P. & M. Curie in Paris, he deciphered the nervous system controlling fish gill exchanges. \r\nAfterward, at the CNRS in Paris, he greatly improved the understanding of synapse elimination in the developing rodent cerebellum. Since 1994, after returning to Strasbourg as a CNRS Research Director, his laboratory has become renowned for its expertise in ultrastructural neuroanatomy. \r\nYannick Bailly has made major contributions concerning the synaptic localization of cardinal molecules involved in neurodegenerative diseases, such as amyloid precursor proteins and presenilins in Alzheimer’s disease and prion protein. \r\nHis research group has provided valuable insight into neuronal death mechanisms involved in brain pathologies, in particular in prion diseases.",institutionString:"University of Strasbourg",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"University of Strasbourg",institutionURL:null,country:{name:"France"}}}],coeditorOne:{id:"296506",title:"Dr.",name:"Benoit",middleName:null,surname:"Schneider",slug:"benoit-schneider",fullName:"Benoit Schneider",profilePictureURL:"https://mts.intechopen.com/storage/users/296506/images/system/296506.jfif",biography:"Benoit Schneider, born in 1973, is a former student of the Ecole Normale Supérieure (Cachan). In 2001 he obtained a Ph.D. in biochemistry and enzymology at the Pasteur Institute (Paris) on the activation of antiretroviral drugs by kinases of the nucleoside salvage pathway. Then he moved to the National Center for Scientific Research of France (CNRS) as a post-doctoral fellow to work on neuronal differentiation and prions, and subsequently got a tenured position. In 2007, he became Professor at the Ecole Polytechnique and in 2014 he has been promoted to research director in the CNRS. His team focuses on studying signaling events sustaining neurodegeneration in prion diseases and other amyloid-based neurodegenerative diseases. Major contributions of his research group concern the physiological roles of cellular prion protein and the identification of neurodegenerative mechanisms common to prion and Alzheimer’s diseases.",institutionString:"Paris Descartes University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Paris Descartes University",institutionURL:null,country:{name:"France"}}},coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"6",title:"Biochemistry, Genetics and Molecular Biology",slug:"biochemistry-genetics-and-molecular-biology"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"247041",firstName:"Dolores",lastName:"Kuzelj",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/247041/images/7108_n.jpg",email:"dolores@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"3545",title:"Autophagy - A Double-Edged Sword",subtitle:"Cell Survival or Death?",isOpenForSubmission:!1,hash:"62f2a3697cfbfa51f5d78b86b07140aa",slug:"autophagy-a-double-edged-sword-cell-survival-or-death-",bookSignature:"Yannick Bailly",coverURL:"https://cdn.intechopen.com/books/images_new/3545.jpg",editedByType:"Edited by",editors:[{id:"164577",title:"Dr.",name:"Yannick",surname:"Bailly",slug:"yannick-bailly",fullName:"Yannick Bailly"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"6694",title:"New Trends in Ion Exchange Studies",subtitle:null,isOpenForSubmission:!1,hash:"3de8c8b090fd8faa7c11ec5b387c486a",slug:"new-trends-in-ion-exchange-studies",bookSignature:"Selcan Karakuş",coverURL:"https://cdn.intechopen.com/books/images_new/6694.jpg",editedByType:"Edited by",editors:[{id:"206110",title:"Dr.",name:"Selcan",surname:"Karakuş",slug:"selcan-karakus",fullName:"Selcan Karakuş"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophanides",surname:"Theophile",slug:"theophanides-theophile",fullName:"Theophanides Theophile"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1373",title:"Ionic Liquids",subtitle:"Applications and Perspectives",isOpenForSubmission:!1,hash:"5e9ae5ae9167cde4b344e499a792c41c",slug:"ionic-liquids-applications-and-perspectives",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/1373.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"57",title:"Physics and Applications of Graphene",subtitle:"Experiments",isOpenForSubmission:!1,hash:"0e6622a71cf4f02f45bfdd5691e1189a",slug:"physics-and-applications-of-graphene-experiments",bookSignature:"Sergey Mikhailov",coverURL:"https://cdn.intechopen.com/books/images_new/57.jpg",editedByType:"Edited by",editors:[{id:"16042",title:"Dr.",name:"Sergey",surname:"Mikhailov",slug:"sergey-mikhailov",fullName:"Sergey Mikhailov"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"55038",title:"Animal Successional Pathways for about 200 Years Near a Melting Glacier: A Norwegian Case Study",doi:"10.5772/intechopen.68192",slug:"animal-successional-pathways-for-about-200-years-near-a-melting-glacier-a-norwegian-case-study",body:'\n
\n
1. Introduction
\n
Due to climate change, glaciers are shrinking worldwide [1–3]. Simultaneously, ‘waves’ of different organisms try to colonise the newly exposed land. Glacier forelands give unique possibilities for studying primary succession. Instead of monitoring changes within a fixed plot over time, which indeed would be a very time-demanding approach, successions can be described by studying sites with known ages. Such a gradient in the terrain, where space is used as a substitute for time, is called a chronosequence [4, 5].
\n
Most studies in glacier forelands have dealt with plant succession, and a thorough and long-lasting one has been performed near the glacier Storbreen in Norway [4]. A main conclusion is that age alone cannot predict the plant community. Local variations in microtopography, moisture, nutrients, substrate and exposure contribute in shaping the species composition. Instead of ending up with a ‘monoclimax’, the succession produces a ‘polyclimax’ with a mosaic of plant communities. A ‘bulk’ succession related only to the age of the ground contains ‘noise’ from a mixture of successional pathways. It has been argued for a ‘geo-ecological’ view on primary succession, where both biotic and abiotic factors were taken into considerations [4]. A recent study from Nigardsbreen foreland in Norway confirmed the modifying effect of microtopography on the floral succession [6].
\n
Studies on animal succession near receding glaciers are fewer, and are mainly focusing on arthropods. In addition to the present case study, there are studies on arthropod succession in glacier forelands from the Alps [5, 7–11], from Svalbard [12–15], from Iceland [16] and from Norway [17–23].
\n
In the following presentation, we have adopted the geo-ecological perspective. In other recent studies of invertebrate successions in Norwegian glacier forelands, a geo-ecological perspective has been successfully applied, when comparing succession patterns at different altitudes and climatic conditions [19, 20, 22, 23].
\n
Hardangerjøkulen glacier in Southern Norway has been receding since the end of ‘the little ice age’ for about 250 years ago. The melting rate has been especially high during the last two decades, with about 20-m retreat yearly at one glacier snout near Finse (Midtdalsbreen). We have good data on earlier positions of the ice edge in this glacier foreland due to dated moraines. Since 2001, extensive zoological studies have been performed here to describe and understand arthropod succession patterns (Figure 1). These studies include soil-living microarthropods [24, 25], surface active beetles, spiders and harvestmen [26, 27], aerial transport of arthropods [28], studies on ancient carbon released by the glacier [29, 30], food choice of pioneers [31], as well as a special focus on early succession [32].
\n
Figure 1.
Sampling of soil for the extraction of microarthropods (springtails and mites). This snow bed habitat was 180 years old and situated 1 km from the glacier (plot no. 18). It had a well-developed Salix herbacea vegetation and a 2-cm thick organic soil layer. Microtopography in the surroundings created gradients from dry ridges to moist depressions.
\n
Time has come to combine these fragments into a holistic story about animal succession near a melting glacier. In addition to summing up the main results from these nine papers, the present syntheses will discuss some general aspects of succession:
\n
Comparison between botanical and zoological succession
Are there alternative succession patterns for arthropods, in the same way as there are alternative succession patterns for vegetation [4–6, 33]?
Is there a strong progressive succession of arthropods on terrain ages of 20–50 years, as in plants [33]?
Does animal and botanical successions differ in their early phases?
Questions about zoological succession
Do most arthropod species tend to persist after colonising [19, 20, 22, 23]?
Is a geo-ecological perspective fruitful and relevant when considering mechanisms of facilitation and inhibition in zoological successions?
Does arthropod succession pattern differ between surface-active macroarthropods and soil-living microarthropods?
Is soil fauna succession in Salix herbacea snow bed vegetation related to the gradual development of an organic layer?
Question about methods
Are sampling methods, material size and taxonomic resolution critical factors when studying arthropod succession?
\n
\n
\n
2. Materials and methods
\n
\n
2.1. Study site
\n
The study site was situated close to the 73-km2 large Hardangerjøkulen glacier in central Southern Norway, between 1200 and 1400 m a.s.l., in the treeless low- and mid-alpine zone. Figure 2 shows the foreland of a northern glacier snout named Midtdalsbreen (60°34′N, 7°28′E).
\n
Figure 2.
Aerial photograph of the glacier foreland, showing the position of moraines from 1750, 1934 and 2005. Beetles, spiders and harvestmen were sampled on the six numbered plots with the following ages: 3, 40, 63, 79, 170 and 205 years. Springtails and mites were sampled from eight plots in zone A (32–48 years), five plots in zone B (52–66 years), seven plots in zone C (72–227 years) and five plots in zone D (10,000 years). The small map shows the position of the foreland in Southern Norway. Modified from Ref. [26].
\n
\n
\n
2.2. Microarthropod sampling
\n
For the study of soil-living microarthopods, which are springtails (Collembola) and mites (Acari), we chose to keep the vegetation factor as constant as possible. All soil samples were taken in S. herbacea vegetation, which was found throughout the gradient. This tiny shrub belongs to the pioneer plants and shows no preference for snow cover on relatively young terrain [34, 35]. However, after about 70 years, it is mainly restricted to patches where snowmelt is late (the so-called snow beds), where it forms rather continuous carpets. Plots 1–8 (zone A) were 32–48 years old, plots 9–13 (zone B) were 52–66 years old and plots 14–20 (zone C) were 72–227 years old. Plot nos. 21–25 (zone D) were outside the 1750 moraine which mark the end of the ‘little ice age’ in Norway, so these five plots had an age of about 10,000 years [24]. In each of the 25 study plots, microarthropods were extracted from 10 to 16 soil cores, 3 cm deep and with a surface area of 10 cm2 [24].
\n
\n
\n
2.3. Macroarthropod sampling
\n
A different sampling strategy was chosen for surface-active macroarthropods, which were beetles (Coleoptera), spiders (Aranea) and harvestmen (Opiliones). Here, we aimed at collecting as many species as possible at each age, by covering a span of vegetation types. Pitfall traps were used at six sites with the following ages: 3, 40, 63, 79, 170 and 205 years. Twenty traps with a diameter of 6.5 cm were operated at each site and emptied every 2 weeks during two snow-free seasons (2007 and 2008) [26]. Traps were usually distributed in a topographic gradient from dry, lichen-dominated vegetation via an Empetrum hermaphroditum heath, to moist snow bed. In a nearby foreland of the same glacier (Blåisen glacier snout), these plant communities were characteristic products of succession [35]. Vegetation and the degree of cover were noted around each trap, a number of soil moisture data were taken, and catches from each trap and period were kept separate. Pitfall traps measure surface activity and not density, but they catch a high number of species and may be used in comparison between sites.
\n
Aquatic invertebrates, for instance larvae of Chironomidae midges, were sampled from young ponds using a sieve. Figure 3 shows a pond on an 8-year-old moraine.
\n
Figure 3.
A small pond, 8 years old, in which larvae of chironomid midges assimilated ancient carbon from the sediments. From Ref. [30].
\n
\n
\n
2.4. Sticky traps and fallout traps
\n
We performed extensive sampling of airborne arthropods on 3–6-year-old ground on the 2005 moraine [28]. Two types of traps were used: sticky traps and fallout traps. The sticky traps were placed on poles, up to 1-m height, and turned towards different directions. Fallout traps had their rim 5 or 11 cm above the ground to prevent surface-active arthropods to drop into them (Figure 4).
\n
Figure 4.
Sticky traps on a 6-year-old moraine, collecting airborne invertebrates from different directions, up to 1-m height. Inserted: open fallout trap with diameter 6.5 cm. From Ref. [28].
\n
\n
\n
2.5. Gut content analyses
\n
The food choice of different species was studied by analysing their gut contents under the microscope. Crop and gut contents of beetles and harvestmen were dissected out and spread on slides, embedded in glycerol. In most springtails, gut contents could be observed in ordinary slides for species identification. The large, spherical species Bourletiella hortensis was squeezed on the slide to spread the gut content.
\n
\n
\n
\n
3. Succession patterns
\n
\n
3.1. Succession in species numbers
\n
Figure 5 illustrates the cumulative species number for oribatid mites [24], springtails [25], and beetles and spiders [26], with increasing age of the ground. All groups showed a rapid addition of species during the first 80 years. Later, relatively few new species colonised among beetles and springtails. Oribatid mites and spiders, however, increased their cumulative species number considerably during the following 150 years. The five plots in 10,000-year-old soil had about the same number of springtail species as in 200-year-old soil, and with very few new species. Among oribatid mites, six new species were added. Beetles and spiders were not sampled in 10,000-year-old soil.
\n
Figure 5.
Cumulative species number of different arthropod groups, with increasing age of the ground. Surface active beetles (Coleoptera) and spiders (Araneae) were pitfall-trapped in various vegetation types, while springtails (Collembola) and oribatid mites (Oribatida) were extracted from soil in Salix herbacea vegetation. From Ref. [26].
\n
\n
\n
3.2. Succession in dominance structure
\n
Another way of presenting succession is to examine the relative dominance among species. To illustrate the main changes among mites and springtails, data were lumped into the four mentioned age groups A–D (Figure 2), each with a similar number of sampling sites. In zone A (32–48 years), oribatids as a group made up about 80% of all mites, but this value later stabilised around 55%. The mite group Actinedida correspondingly increased their dominance in older soil, while predatory Gamasina mites were rare throughout the age gradient. In 10,000-year-old soil, the pioneer species Tectocepheus velatus was still present, but Oppiella neerlandica was now the dominant oribatid species [24].
\n
Also, the springtail community showed considerable changes in dominance structure as the soil aged [25]. Two Folsomia species took over the dominance in zone B (52–66 years), and later Tetracanthella brachyura became the most abundant species. In short, there was a ‘Folsomia front’ approaching the pioneer ground, and behind it followed a ‘Tetracanthella front’. The dominance structure of springtails was surprisingly similar in zone C (72–227 years) compared to the very old soil of 10,000 years in zone D [25].
\n
Pitfall catches of beetles and spiders indicated clear changes in community structure during the 200-year study period. This is illustrated for beetles in Table 1, which shows the relative catches of the 13 most common species. Bembidion hastii dominated the catches strongly on 3-year-old ground but was still well represented on 40-year-old ground, for then to disappear on older ground. On 63- and 79-year-old ground, the community structure was very similar, being dominated by Amara quenseli and Patrobus septentrionis. While A. quenseli became very rare in older sites, P. septentrionis increased its dominance further and represented more than half of the catches on 160-year-old ground. However, on 205-year-old ground, Liogluta alpestris from the Staphylinidae family took over the dominance.
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
Species
\n
Family
\n
3 yr
\n
40 yr
\n
63 yr
\n
79 yr
\n
160 yr
\n
205 yr
\n
\n\n\n
\n
Bembidion hastii
\n
Carabidae
\n
81
\n
22
\n
0
\n
0
\n
0
\n
0
\n
\n
\n
Nebria nivalis
\n
Carabidae
\n
10
\n
10
\n
1
\n
2
\n
<1
\n
<1
\n
\n
\n
Amara alpina
\n
Carabidae
\n
4
\n
23
\n
4
\n
4
\n
4
\n
10
\n
\n
\n
Geodromicus longipes
\n
Staphylinidae
\n
2
\n
10
\n
6
\n
4
\n
2
\n
2
\n
\n
\n
Simplocaria metallica
\n
Byrrhidae
\n
1
\n
9
\n
<1
\n
<1
\n
0
\n
0
\n
\n
\n
Amara quenseli
\n
Carabidae
\n
2
\n
0
\n
30
\n
21
\n
<1
\n
1
\n
\n
\n
Curimopsis cyclolepidia
\n
Byrrhidae
\n
0
\n
0
\n
9
\n
3
\n
0
\n
0
\n
\n
\n
Nebria rufescens
\n
Carabidae
\n
0
\n
1
\n
2
\n
9
\n
<1
\n
<1
\n
\n
\n
Patrobus septentrionis
\n
Carabidae
\n
0
\n
23
\n
29
\n
32
\n
54
\n
13
\n
\n
\n
Cymindis vaporariorum
\n
Carabidae
\n
0
\n
0
\n
5
\n
1
\n
13
\n
1
\n
\n
\n
Liogluta alpestris
\n
Staphylinidae
\n
0
\n
0
\n
7
\n
11
\n
6
\n
34
\n
\n
\n
Anthophagus alpinus
\n
Staphylinidae
\n
0
\n
0
\n
0
\n
1
\n
6
\n
13
\n
\n
\n
Chrysomela collaris
\n
Chrysomelidae
\n
0
\n
0
\n
0
\n
<1
\n
2
\n
8
\n
\n
\n
Other species
\n
\n
0
\n
2
\n
6
\n
10
\n
10
\n
16
\n
\n
\n
Total percentage
\n
\n
100
\n
100
\n
100
\n
100
\n
100
\n
100
\n
\n\n
Table 1.
Dominance structure of the beetle community on the ground of different age, expressed by pitfall catches. For each species, the highest dominance value is shown in bold numbers. Species with dominance values below 5% are collectively listed under ‘Other species’.
\n
Table 2 lists the 14 most common spider species in the pitfall material. The catches on newly deglaciated ground was dominated by Pardosa trailli, Erigone tirolensis and E. arctica, while Collinsia holmgreni and Hilaira cf. frigida took over the dominance after 40 years. As in beetles, spiders showed a similar community structure on 63- and 79-year-old ground, being dominated by Tiso aestivus, Arctosa alpigena and E. arctica. After 160 years, Scotinotylus evansi dominated the catches, and P. paludicola dominated after 205 years.
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
Species
\n
Family
\n
3 yr
\n
40 yr
\n
63 yr
\n
79 yr
\n
160 yr
\n
205 yr
\n
\n\n\n
\n
Pardosa trailli
\n
Lycosidae
\n
36
\n
14
\n
6
\n
26
\n
1
\n
1
\n
\n
\n
Erigone tirolensis
\n
Linyphiidae
\n
27
\n
19
\n
1
\n
1
\n
2
\n
<1
\n
\n
\n
Erigone arctica
\n
Linyphiidae
\n
25
\n
2
\n
16
\n
18
\n
0
\n
<1
\n
\n
\n
Collinsia holmgreni
\n
Linyphiidae
\n
12
\n
34
\n
1
\n
2
\n
3
\n
1
\n
\n
\n
Hilaira cf. frigida
\n
Linyphiidae
\n
0
\n
30
\n
5
\n
10
\n
7
\n
6
\n
\n
\n
Tiso aestivus
\n
Linyphiidae
\n
0
\n
0
\n
34
\n
23
\n
9
\n
19
\n
\n
\n
Arctosa alpigena
\n
Lycosidae
\n
0
\n
0
\n
26
\n
17
\n
11
\n
0
\n
\n
\n
Scotinotylus evansi
\n
Linyphiidae
\n
0
\n
0
\n
6
\n
2
\n
26
\n
5
\n
\n
\n
Pelecopsis mengei
\n
Linyphiidae
\n
0
\n
0
\n
3
\n
0
\n
12
\n
<1
\n
\n
\n
Pardosa septentrionalis
\n
Lycosidae
\n
0
\n
0
\n
0
\n
0
\n
8
\n
0
\n
\n
\n
Ozyptila arctica
\n
Thomisidae
\n
0
\n
0
\n
0
\n
0
\n
8
\n
<1
\n
\n
\n
Pardosa paludicola
\n
Lycosidae
\n
0
\n
0
\n
0
\n
0
\n
<1
\n
39
\n
\n
\n
Oedothorax retusus
\n
Linyphiidae
\n
0
\n
0
\n
0
\n
0
\n
0
\n
10
\n
\n
\n
Gonatium rubens
\n
Linyphiidae
\n
0
\n
0
\n
0
\n
0
\n
1
\n
8
\n
\n
\n
Other species
\n
\n
0
\n
1
\n
2
\n
1
\n
12
\n
11
\n
\n
\n
Total percentage
\n
\n
100
\n
100
\n
100
\n
100
\n
100
\n
100
\n
\n\n
Table 2.
Dominance structure of the spider community on the ground of different age, expressed by pitfall catches. For each species, the highest dominance value is shown in bold numbers. Species with dominance values below 5% are collectively listed under ‘Other species’.
\n
\n
\n
3.3. Do species persist after colonisation?
\n
A study of macroarthropod succession in several Norwegian glacier forelands at different altitudes and environmental conditions concluded that most species persisted after colonisation [19, 20, 22, 23]. This was regarded as a fundamental difference as compared to plant succession patterns. However, the taxonomic resolution in these studies was low in certain animal groups. For instance, in the beetle family Staphylinidae, which was represented by a high number of species in our study (21 out of 40 beetles), most species were unidentified in the studies by Vater and Matthews. The number of traps used in their studies was also low in some cases.
\n
The more extensive material from the present case study, where all beetles and spiders were identified to species, confirmed to a large degree the hypothesis of ‘adding and persistence’ of species [26]. However, there were some exceptions. Among beetles, B. hastii disappeared when vegetation became more or less closed after about 60 years. Simplocaria metallica became very rare at the same time, and was not recorded after about 80 years. Likewise, the cold-adapted Nebria nivalis nearly disappeared after 80 years. Curimopsis cyclolepidia was only recorded in the range of 60–80 years. This range was also the clearly preferred for A. quenseli. It is interesting to note that the two last-mentioned species were not found in an extensive pitfall trapping in various neighbouring habitats of 10,000 years of age during 3 years (1969–1971) [36].
\n
Among spiders, P. trailli and E. arctica nearly disappeared after 80 years. A. alpigena was numerous between 60 and 160 years but was absent after about 200 years. At 160 years, two new species appeared as very common: P. septentrionalis and Ozyptila arctica, but the first one disappeared in 200-year-old soil, and the second one nearly so.
\n
Most soil microarthropods seemed to persist after colonisation. Among oribatid mites, the pioneer species Liochthonius cf. sellnicki was barely present after about 70 years [24]. Among springtails, the cold-loving pioneer species Agrenia bidenticulata gave up after about 50 years. There were several examples of rare species which ‘disappeared’ in older soils, but the small data do not allow firm conclusions about the presence or absence.
\n
A general persistence of both macro- and microarthropods during succession indicates that these species have a high tolerance for each other and for changes in vegetation. Obviously, the concept of tolerance is as important as facilitation and inhibition when we try to understand succession.
\n
\n
\n
3.4. Relations to environmental parameters
\n
\n
3.4.1. Parameters related to age
\n
A detrended correspondence analysis (DCA) showed that terrain age was strongly correlated to the distance to the glacier, increased organic content in soil and falling pH values [24]. A species biplot of a DCA for mites sorted pioneer species, seral species and late seral species rather well into groups, confirming a successional process [24]. Correspondingly, a non-metric multidimensional scaling (NMDS) plot for springtails separated well the pioneer species. However, in contrast to the mite succession, which showed considerable difference between 72–227 years and 10,000 years, the NMDS plot for springtail communities confirmed a great similarity in these two age groups [25]. Concerning beetle and spider succession, an NMDS plot showed a clear succession, and the pioneer species were best separated. Also, vegetation cover was correlated with age and distance from the glacier [26].
\n
Figure 6 shows a linear relation between age and thickness of the organic layer (R = 0.83, F = 38.4, P < 0.001). However, the variation was large. In Figure 7, species numbers of springtails and oribatid mites were related to the depth of the organic layer, and adapted curves indicated that species numbers tended to flatten out at about 10-mm organic layer. Pioneer microarthropod species have to be independent of an organic layer, and able to live on or close to the surface. Surface-living species are called epedaphic, litter-dwellers hemiedaphic and deeper-living species euedaphic. The two first groups typically contain larger species with eyes and pigmented body, while euedaphic species are often small, white and blind. Figure 8 shows the number of springtail species from each of these categories (or transition categories) in soil of different age groups. It is a bit surprising that some hemiedaphic, and even two euedaphic species were recorded already in soil of 32–48 years of age. However, their presence may not be permanent. The organic layer was absent up to 36 years, and 1–3-mm thick in 41–48-year-old plots [24]. Later, the hemiedaphic species gradually became numerous, with 14 species in the very old soil. About five euedaphic species were established already at the age of 52–66 years, and the species number in this category changed little with further age.
\n
Figure 6.
Relationship between the age of soil and the thickness of the organic layer.
\n
Figure 7.
Adapted curves for the relationship between the thickness of the organic layer in soil and species numbers of springtails (Collembola) and oribatid mites (Oribatida).
\n
Figure 8.
Structure of the springtail (Collembola) community at different age groups of the soil. E = Edaphic species, which are surface-living. H = Hemiedaphic species, which are litter dwellers. EU = Euedaphic species, which are deeper-living soil species.
\n
\n
\n
3.4.2. A ‘wet’ and a ‘dry’ successional pathway
\n
Local variation in soil moisture modified the succession pattern, among both surface-active macroarthropods and soil-living microarthropods. Direct correlation between soil moisture measured close to single traps, and the species collected there showed that the following beetles significantly preferred moist soil: P. septentrionis, Geodromicus longipes and L. alpestris. Three other species were clearly dry-ground dwellers: Byrrhus fasciatus, Cymindis vaporariorum and A. quenseli [37]. Among spiders, T. aestivus is an example of a dry-ground dweller, as all of 102 specimens were collected on dry ridges with lichen-dominated vegetation.
\n
In Figure 9, the ‘noise’ from varying moisture conditions was identified by separating catches of beetles from typical ‘wet’ and typical ‘dry’ traps. We see how P. septentrionis dominated strongly in wet sites, while A. quenseli dominated the catches in dry sites nearby.
\n
Figure 9.
Effect of wet and dry ground on the structure of the beetle community. Both on 63- and 79-year-old plots, Patrobus septentrionis (Pa. se.) dominated on wet ground, and Amara quenseli (Am. qu.) on dry ground. Full names of the other species are Amara alpina (Am. al.), Curimopsis cyclolepidia (Cu. cy.), Cymindis vaporariorum (Cy. va.), Geodromicus longipes (Ge. lo.), Liogluta alpestris (Li. al.) and Nebria rufescens (Ne. ru.).
\n
Earlier studies have considered soil moisture to be the most important ecological factor for ground-living beetles in Norwegian alpine areas [38]. This is in accordance with our results. We conclude that surface-active macroarthropods followed two parallel successional trends in the foreland: a dry and a moist pathway.
\n
Also, the succession of soil animals was affected by moisture. In the same glacier foreland, oribatid mites have been studied on dry moraine ridges of known age [18]. This allows for a comparison of the oribatid community in dry soil with neighbouring, moist snow bed soil at two age groups: 45–47 years and 66–72 years. While the generalist T. velatus was well represented in both dry and wet habitats, L. lapponicus and Camisia horrida occurred only on dry ridges, and L. cf. sellnicki and C. foveolata only in the wet snow bed. This illustrates that species within the same genus may have quite different moisture preferences.
\n
Figure 10 illustrates schematically wet and dry succession. Both pathways were reflected in the vegetation mosaic of the foreland. This is in accordance with studies in the Rotmoos foreland in the Austrian Alps, where the effect of local topography and exposure on various invertebrate groups was studied. The moisture regime was an important factor on a local scale, for all site ages [7].
\n
Figure 10.
While the pioneer community is rather predictable, the further succession pattern differs in dry and wet patches. The figure shows a characteristic beetle and oribatid mite for a ‘dry’ and ‘wet’ succession, respectively.
\n
\n
\n
\n
3.5. Succession of surface animals versus soil animals
\n
Among both surface-active macroarthropods and soil-living microarthropods, species numbers increased markedly during the first 80 years. Both groups had species that were favoured by the glacier retreat, because either they were cold-adapted (the springtail A. bidenticulata and the carabid beetle N. nivalis) or they preferred open space (the springtail B. hortensis and the carabid beetle B. hastii). Furthermore, both surface-living and soil-living animals were split into a ‘dry’ and a ‘moist’ succession pattern.
\n
The two groups were, however, differently related to the development of vegetation. Soil-living microarthropods were favoured by the gradual development of an organic soil layer. Surface-active macroarthropods were influenced by the gradual closing of vegetation, and for some, to the appearance of food plants. While predators dominated throughout succession among macroarthropods, there were fewer predator species among microarthropods.
\n
\n
\n
3.6. Comparison between plant and animal succession
\n
Several investigators have detected a peak in plant richness early in primary succession, followed by a decline due to increased competition [4]. For instance, in the nearby foreland of Blåisen, there was an early diversity peak in proximal slopes [34]. Most arthropods, however, tend to persist after colonisation (see below), and there is no early peak in species richness.
\n
Plant and animal succession have several features in common. Both plants and animals respond to local soil moisture, resulting in a ‘dry’ and a ‘wet’ succession. In the foreland of Blåisen, microtopography and moisture clearly affected plant succession [34].
\n
Another similarity between botanical and zoological succession in the Finse area is that it takes at least 200 years to establish a stable “climax” community. Near Blåisen, only communities of simple structure, such as snow beds, reached a mature state after 220 years of succession [39].
\n
Furthermore, plants with very narrow niches could attain local optima during early succession on glacier forelands and nunataks [35, 40]. Examples were Draba cacuminum, Poa herjedalica, P. jemtlandica and Sagina intermedia in the Finse area. Corresponding arthropod examples are two open space-living species: the carabid beetle B. hastii and the springtail B. hortensis, as well as two cold-loving species: the carabid beetle N. nivalis and the springtail A. bidenticulata.
\n
A general similarity between plant and animal succession in Norwegian forelands is that the process is markedly affected by altitude and local climate. Glacier forelands in a harsh climate at high altitudes create a slow and species-poor succession, while the succession in both plants and animals is rapid and species-rich in forelands situated below the tree line [4, 19, 20, 22, 23].
\n
\n
\n
3.7. Pioneer arthropods—a heterogenic group
\n
The pioneer community was an interesting mix of generalists and specialists, and of various life strategies [26, 32]. Among early springtails and mites, there were both parthenogenetic and bisexual species, and species with either a short or a long life cycle [24, 25]. Furthermore, there were open-ground species as the springtail B. hortensis and the carabid beetle B. hastii, and ‘cold-loving’ species represented by the springtail A. bidenticulata and the carabid beetle N. nivalis. Several generalists colonised the pioneer ground. The harvestman Mitopus morio is a generalist predator, with high catches throughout the whole foreland [27]. Among oribatid mites, T. velatus is a well-known generalist, and among springtails we can point at Desoria olivacea and Isotoma viridis. The carabid A. alpina and the staphylinid G. longipes are habitat-tolerant beetles, and E. tirolensis is a spider example. Despite differences in ecology, pioneer arthropods have certain key abilities in common: they are good dispersers and can live, eat and reproduce on barren or nearly barren ground [32].
\n
\n
\n
\n
4. Dispersal: how to get there?
\n
The rapid colonisation of newly exposed ground indicated that arthropods have a high dispersal ability. On Iceland, springtails and oribatid mites easily colonised recently emerged nunataks, and isolation of a few kilometres did not affect the colonisation [16]. These results strongly indicate aerial dispersal, and our study supports this. Fallout traps and sticky traps collected nine species of springtails and four species of oribatid mites, as well as some Actinedida mites and spiders (Figure 11). Among other items were unwinged aphids, some flies, several chironomid midges, a few seeds, and many fragments and diaspores of pioneer mosses [28]. Most aerial transport occurred rather close to the ground, below 0.5-m height. Sand grains in sticky traps up to this level illustrated the mechanical force of wind transport.
\n
Figure 11.
Invertebrates taken in sticky traps, proving airborne transport. A = the springtail Bourletiella hortensis. B = the mite Bryobia sp. C = the mite Tectocepheus velatus. D = the spider Erigone arctica. From Ref. [28].
\n
Some of the trapped species were assumed not to be able to thrive on pioneer ground, but to depend on a thicker organic layer [28]. In that case, their dispersal ability is high, but the pioneer ground may act as a ‘sink’ for them, where they will die. A ‘real’ pioneer species must be able both to arrive, to tolerate the harsh environmental conditions, to manage competition, to find food and to reproduce. In other words, pioneers must pass two ‘filters’: a ‘dispersion filter’ to arrive and an ‘ecological filter’ to establish a population.
\n
\n
\n
5. Food sources: how to survive?
\n
How can so many arthropod species—even predators—thrive on bare ground, before higher plants have established, or are very few? Based on analyses of the gut contents in springtails, beetles, harvestmen and chironomid midge larvae, we found that there were three ‘invisible’ food sources on newly deglaciated ground: biofilm with diatom algae, tiny pioneer mosses and ancient carbon delivered by the glacier.
\n
\n
5.1. Terrestrial biofilm as food
\n
The springtail A. bidenticulata (Figure 12) was a ‘super-pioneer’, following the retreating glacier edge closely. Their guts contained a rather compact material dominated by tiny mineral particles, but diatom algae could often be seen [29, 31] (Figure 13). We assume that mineral particles were ingested accidentally when ‘grazing’ on biofilm. Terrestrial diatoms have the ability to establish a slimy, nutrient-rich biofilm on open ground by producing large quantities of extracellular polymeric substances [41, 42]. Diatom algae were also found in some guts of two other pioneer springtails: D. olivacea and I. viridis [31]. The early presence of terrestrial diatom algae shows that chlorophyll-based food chains start almost immediately after deglaciation.
\n
Figure 12.
Some pioneer invertebrates. A = the biofilm-eating springtail Agrenia bidenticulata. B = the moss-eating springtail Bourletiella hortensis, together with four bulbils (dispersal units) of the moss Pohlia sp. C = newly hatched adult and a larva of the moss-eating beetle Simplocaria metallica. From Ref. [31].
\n
Figure 13.
Hind part of the springtail Agrenia bidenticulata showing diatom algae in the gut. Most diatoms are densely packed, but a single one is easily seen to the left. From Ref. [31].
\n
\n
\n
5.2. Pioneer mosses as food
\n
Already on a four-year-old ground, five mosses were observed: Ceratodon purpureus, Bryum arcticum, Pohlia filum, Racomitrium canescens and Funaria hygrometrica [31]. On nunataks of Omnsbreen glacier, about 10 km further North, a similar pioneer moss community has been found [40].
\n
Due to characteristic cell structure in each moss species or genus, it was possible to identify small moss fragments in arthropod guts. On a 3-year-old ground, the relatively large and spherical springtail B. hortensis (Figure 12) had eaten leaves of C. purpureus, Bryum sp. and Pohlia sp., as well as nutrient-rich dispersal units (bulbils) of P. filum [31]. Among beetles, the family Byrrhidae is known to have moss-feeders, and on a six-year-old ground, guts of S. metallica (Figure 12) contained three different mosses (Figure 14). Two carabid beetles on three–six-year-old ground were omnivores, as their guts contained both invertebrates and moss fragments: A. alpina and A. quenseli. Conclusively, as much as four pioneer arthropods grazed on pioneer mosses [31].
\n
Figure 14.
Moss fragments recorded in the gut of the beetle Simplocaria metallica. A = cross sections of a moss stem. B = leaf of Pohlia sp. C = leaf of Ceratodon purpureus. D = typical cell structure of a Bryum leaf. From Ref. [31].
\n
\n
\n
5.3. Ancient carbon as food
\n
The identification of this food resource was gradual, and surprising. A publication from a glacier foreland in the Austrian Alps showed that heterotrophic microbial communities used ancient carbon released by the glacier [43]. We wondered whether ancient carbon was released also by our glacier, and if so, whether it could be used as a nutrient source for pioneer arthropods. In September 2010, samples of surface soil (sand and silt) were taken 20 m from the glacier edge. During that summer, the glacier had retreated as much as 34 m. Analyses by Beta Analytic in Florida concluded that the samples contained material which was in average 21,000 years old. Furthermore, radiocarbon dating of chironomid midges and four predators, which were pitfall trapped on a 6–7-year-old ground showed that they all contained ancient carbon. The wolf spider P. trailli, had a radiocarbon age of 340 years, the harvestman M. morio 570 years, the carabid beetle N. nivalis 690 years, the carabid beetle B. hastii 1100 years and chironomid midges 1040 years [29]. Even larvae and adults of predatory diving beetles collected in young ponds had a radiocarbon age of 1100–1200 years. Springtails, however, did not contain ancient carbon.
\n
New samples of surface soil taken close to the ice edge 4 years later corrected the age of released organic material to about 5160 years [30]. In the latter analysis, samples were pretreated at a lower temperature so that possible graphite particles from the phyllite-containing bedrock were not combusted and included in the analysis.
\n
We found that chironomid larvae living in the sediment of young ponds assimilated the ancient carbon, and achieved a radiocarbon age up to 3270 years. We assumed that these larvae were eaten by diving beetles (Figure 15), and that adult midges ending on the soil surface after swarming fed terrestrial predators. Studies of the gut contents of the carabid beetles N. nivalis and B. hastii, and the harvestman M. morio confirmed that adult chironomid midges were an important part of their diet.
\n
Figure 15.
These pond-living invertebrates contained ancient carbon supplied by the melting glacier. A = sediment with chironomid larvae in tubes. B = chironomid larvae which have been partly freed from their tubes. C = two predacious larvae of the diving beetle Agabus bipustulatus, and three saprophagous, cylindrical larvae of Tipulidae (crane flies). D = adult predacious diving beetle, Agabus bipustulatus. From Ref. [30].
\n
To be sure that ancient carbon was assimilated into the body tissue, measurements were also made on the larvae of Tipulidae (another Diptera group) in the same pond sediment, being careful to remove the gut contents before analysis. The actual body tissue from Tipulidae larvae had a radiocarbon age of 1610 years [30]. Moreover, chironomid larvae collected in the glacier river, and freed from their gut contents, had radiocarbon ages up to 1260 years.
\n
We concluded that ancient organic material released by the glacier was assimilated by chironomid larvae, and transported further to aquatic and terrestrial predators. Chironomid midges thus supported early succession, and bound aquatic and terrestrial food webs together [29, 30].
\n
The remaining question was: What is the source of the ancient carbon that had been stored in the glacier? We gradually abandoned the possibility that it came from old forest, bogs or soils from earlier periods where the glacier had been periodically absent. One reason was that the actual organic particles were probably extremely small. A purely chemical process, where carbon from non-biological bicarbonate served as a CO2 source for aquatic algae, was also abandoned, since gut contents of chironomid larvae were practically free from algae [30]. Instead, our suspicion was led towards long-transported aerosols, originating from the incomplete combustion of fossil fuels. Such aerosols make up a part of the organic matter that glaciers collect by surface accumulation [30]. These aerosols are C14 depleted, and radiocarbon dating will reveal that they are very old [44, 45]. In fact, heavily glaciated watersheds may transport ancient, bioavailable carbon all the way to oceans, where marine microorganisms can assimilate the old carbon [46]. The aerosol hypothesis would fit with all our results [30].
\n
\n
\n
5.4. A pioneer food web
\n
Pioneer ground of 3–7 years of age contained a surprisingly diversity of food sources for pioneer arthropods (Table 3). Primary production was represented by invisible biofilm with diatom algae, tiny bryophytes and scattered vascular plants. Fungal hyphae found in some springtail guts were early terrestrial decomposers, and chironomids eaten by several predators were (as larvae) detritus feeders on ancient organic material. In addition, some inblown insects certainly contributed as prey. Two ‘super-pioneers’ followed the ice edge most closely: the biofilm-feeding springtails A. bidenticulata and D. olivacea.
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
Species
\n
Group
\n
Biofilm
\n
Fungal hyphae
\n
Bryophytes
\n
Vascular plants
\n
Invertebrates
\n
Ancient carbon via Chironomidae
\n
\n\n\n
\n
Agrenia bidenticulata
\n
Collembola
\n
x
\n
\n
\n
\n
\n
\n
\n
\n
Desoria olivacea
\n
Collembola
\n
x
\n
\n
\n
\n
\n
\n
\n
\n
Isotoma viridis
\n
Collembola
\n
x
\n
x
\n
\n
\n
\n
\n
\n
\n
Lepidocyrtus lignorum
\n
Collembola
\n
\n
x
\n
\n
\n
\n
\n
\n
\n
Bourletiella hortensis
\n
Collembola
\n
\n
x
\n
x
\n
\n
\n
\n
\n
\n
Simplocaria metallica
\n
Coleoptera
\n
\n
\n
x
\n
\n
\n
\n
\n
\n
Amara alpina
\n
Coleoptera
\n
\n
\n
x
\n
x
\n
x
\n
\n
\n
\n
Amara quenseli
\n
Coleoptera
\n
\n
\n
x
\n
\n
x
\n
\n
\n
\n
Nebria nivalis
\n
Coleoptera
\n
\n
\n
\n
\n
x
\n
x
\n
\n
\n
Bembidion hastii
\n
Coleoptera
\n
\n
\n
\n
\n
x
\n
x
\n
\n
\n
Mitopus morio
\n
Opiliones
\n
\n
\n
\n
\n
x
\n
x
\n
\n\n
Table 3.
Food sources of terrestrial invertebrates on 3–6-year-old ground, based on gut content analyses. From Ref. [31].
\n
To understand the food web on pioneer ground, we must combine aquatic and terrestrial food chains, and distinguish between chlorophyll-produced carbon, inblown carbon and ancient carbon released by the glacier. Figure 16 summarises these relationships, and distinguishes between autotrophs, herbivores, predators and decomposers.
\n
Figure 16.
This food web from pioneer ground combines aquatic and terrestrial habitats. Shaded boxes illustrate the flow of ancient carbon, lower boxes with a grey frame show the flow of chlorophyll-produced carbon and the two upper boxes with a black frame show the use of carbon from inblown organic material. It is distinguished between autotrophs, herbivores, predators and decomposers. From Ref. [29].
\n
A pioneer food web can probably be of local character. In the present case, chironomid midges hatching from young ponds fed several terrestrial predators. In the Rotmoos foreland in Austria, however, springtails were found to be the main prey, and intraguild predation was demonstrated [47, 48].
\n
\n
\n
5.5. Feeding categories during succession
\n
Figure 17 shows that throughout the 200-year-old succession, the great majority of trapped macroarthropods were predators. While all spiders are predators, beetles contained a mixture of feeding categories. Pure herbivores were always represented by few species, even in the oldest sites.
\n
Figure 17.
Feeding categories among pitfall-trapped macroarthropods at different ages of the ground. All spiders are predators, while beetles contained various feeding groups. Pure herbivores were rare throughout the gradient. From Ref. [26].
\n
\n
\n
\n
6. Driving forces in various phases of animal succession: facilitating and inhibiting factors
\n
In early succession theory, facilitation, inhibition and tolerance were central concepts [49]. They were all used in a biotic context, and it was assumed that succession was driven by the way species interacted with one other. Early occupants could modify the environment in a way that influenced ‘late-successional’ species in three possible ways: (a) make the habitat more suitable for other species (facilitation) and (b) less suitable (inhibition), or early occupants had little or no effect on subsequent recruitment of species (tolerance). In the following presentation of four characteristic phases of succession, we use the terms facilitation, inhibition and tolerance in both biotic and abiotic contexts. We want to show that animal succession is only partly driven by the development of vegetation, and that abiotic factors may considerably influence the succession process.
\n
\n
6.1. Age 3–7 years: bare ground or only scattered pioneer vegetation
\n
Wind facilitated transport of invertebrates, prey, algae and mosses into newly exposed ground [28]. In a foreland at Svalbard, aerial dispersal of midges and ballooning spiders was even assumed to add nutrients to virgin soil [12, 14].
\n
The glacier itself facilitated the pioneer community by producing ponds, in which chironomid larvae assimilated ancient carbon. Within ponds, chironomid larvae were eaten by predatory diving beetles. Adult midges transported ancient carbon to terrestrial predators [29, 30]. The presence of predators before visible plants, often referred to as the ‘predator first paradox’ [13], can to a large degree be explained by local production of chironomid prey from young ponds. Cold-adapted species, like the springtail A. bidenticulata and the ground beetle N. nivalis, were facilitated by proximity to the glacier. However, a glacier retreat around 20 m per year means that they had to migrate continuously to remain in the cold zone.
\n
\n
\n
6.2. Age about 30–40 years: patchy pioneer vegetation and much open ground
\n
A high soil humidity due to much silt facilitated the colonisation of several plants and animals. Small patches of S. herbacea initiated the production of an organic layer. The moist-loving carabid beetle P. septentrionis colonised the ground. Pitfall catches documented a high surface activity among larger springtail species, not only within vegetated patches but also on bare ground [32].
\n
\n
\n
6.3. Age about 60–200 years: mainly closed vegetation
\n
A closed vegetation created shelter, reduced wind and maintained humidity. Web-building spiders were favoured by a three-dimensional vegetation. The pioneer ground beetle B. hastii disappeared when the vegetation became closed, but a local population survived on a 75-year-old bare patch [21]. The gradually deeper organic soil layer was positive for soil-living springtails and mites (Figures 6–8).
\n
For herbivores, the presence of a suitable food plant is crucial. While the moss-eating beetle S. metallica was found in the first moss patches on a 3-year-old ground, another moss-eating beetle, B. fasciatus, was not trapped until on a 63 years old ground. The beetle Chrysomela collaris feeds on S. herbacea, which occurs throughout the foreland. However, this beetle colonised late, and was found after 79 years. Clearly, other factors than the presence of the food plant determined the colonisation rate of some herbivorous beetles [26].
\n
Both macro- and microarthropods were split into two main successional pathways: a dry and a wet succession. Due to patchy distribution of dry and moist habitats in the foreland, specialist on dry or moist sites had to overcome dispersal over unfavourable ground. The carabid beetle C. vaporariorum, which prefers dry ground, has a disadvantage by its inability to fly, due to rudimentary wings.
\n
\n
\n
6.4. Age about 10,000 years: mature soil
\n
The number of oribatid species increased clearly in this very old soil compared to 200-year-old soil, maybe facilitated by a deep organic layer. However, the increase was small for springtails, which were more efficient in colonising the foreland.
\n
\n
\n
\n
7. Remarks
\n
Glacier forelands offer unique possibilities for the study of succession. We are beginning to understand patterns of arthropod succession by comparing studies from Norway, Svalbard, Iceland and the Alps [26, 32]. Several species or genera among arthropods are common pioneers in Norway and the Alps. However, glacier foreland chronosequences are both variable and complex. More case studies are needed, both to reveal local variations in pioneer communities and succession patterns and to look for general patterns.
\n
Sample size is a critical factor. Figure 18 shows how 12 soil samples within one plot gradually increased the cumulative number of mite taxa, but none of the samples contained all taxa. Ideally, sample numbers should be so high that the cumulative species number stabilises. If species numbers in different sites shall be compared, corresponding sampling effort should be used in all sites. To cover local variation in species composition, it may be better to take several small samples instead of a few larger covering the same area. During sampling with a soil corer, large, surface active springtails may escape by jumping. Some pitfall traps in addition may give valuable information.
\n
Figure 18.
In plot no. 18 (age 180 years, see Figure 1), 12 soil cores were taken. This example shows how the cumulative number of mite taxa increased with increasing number of cores. However, none of the single cores contained all taxa (columns).
\n
Pitfall traps are much used for beetles and spiders in comparative studies, but the number of traps is often low. Even in the present study, with 20 traps operating during 2 years at each site, several species were taken in very few specimens [26]. Traps should be operated throughout the snow-free season, since certain species may have restricted seasonal activity.
\n
The term ‘primary succession’ is questionable when both aquatic and terrestrial pioneer communities use ancient carbon released by the glacier. Young ponds acted as ‘biological oases’ where ancient carbon was assimilated by Diptera larvae, mainly Chironomidae. In a foreland without ponds, a possible release of ancient carbon can be checked by radiocarbon dating chironomid larvae from the glacier river. A peculiar thing is that if invertebrates, which had assimilated old carbon, had been recovered as subfossils and radiocarbon dated, their age had been overestimated by up to 1100 years [29]. Since several pioneer species were herbivores on biofilm or mosses, the present succession did not fit with the ‘predator-first’ hypothesis. Although pioneer species may be ecologically very different, the pioneer community is surprisingly predictable, both within Norwegian forelands and in the Alps, and several genera are in common [32].
\n
We need to improve our knowledge about the autecology of the individual species to better understand their position and functional role in the succession process. From each species´ point of view, colonising the foreland is a question of fulfilling minimum ecological demands. For instance, analyses of gut content were the key to understand the pioneer food web in the present foreland [29, 31]. Experimental studies involving transportation and re-location of species could be rewarding, but would it for the sake of science be ethically acceptable to move species within a ‘natural laboratory’ that should develop in a natural way?
\n
A negative and special aspect by melting glaciers is that their meltdown will threaten cold-adapted invertebrates which live near glaciers. Especially when it comes to endemic, cold-adapted species, melting glaciers represent an extinction threat, as in certain mountains of the Southern Alps [50].
\n
\n
\n
8. Conclusions
\n
The questions posed in the ‘Introduction’ section can be answered in the following way:
\n
Comparison between botanical and zoological succession
Are there alternative succession patterns for arthropods, in the same way as there are alternative succession patterns for vegetation? Yes, also in animal succession, local conditions like topography and moisture modify the succession pattern. Among both soil-living microarthropods and surface-living macroarthropods, we can distinguish between a ‘dry’ and a ‘moist’ succession.
Is there a strong progressive succession of arthropods on terrain ages of 20–50 years, as in plants? The period of fast colonisation among arthropods was longer in this study, around 80 years. Most species of springtails and beetles had arrived at that age, but species numbers of mites and spiders continued to increase substantially also after 80 years.
Do animal and botanical successions differ in their early phases? Yes, in the early phase, animal succession may be said to differ from botanical succession. Before higher plants established, or were represented by very few species, a rather rich assemblage of arthropods was present. However, if we include pioneer mosses and terrestrial diatom algae, chlorophyll-based food chains established very early. In addition, several pioneer arthropods were able to use ancient carbon released by the glacier, being independent of primary production.
Questions about zoological succession
Do most arthropod species tend to persist after colonising? Yes, but there were exceptions. The occurrence of many rare species makes it difficult to answer the question.
Is a geo-ecological perspective fruitful and relevant when considering mechanisms of facilitation and inhibition in zoological successions? Yes, since abiotic factors highly influence colonisation and succession pattern.
Does arthropod succession pattern differ between surface-active macroarthropods and soil-living microarthropods? There were large similarities.
Is soil fauna succession in S. herbacea snow bed vegetation related to the gradual development of an organic layer? Yes, soil fauna succession goes on even after this vegetation type has been permanently established, and species numbers are related to the further development of the soil profile.
Question about methods
Are sampling methods, material size and taxonomic resolution critical factors when studying arthropod succession? Yes.
\n
\n
Acknowledgments
\n
We are grateful for being allowed to reuse figures and tables from Arctic, Antarctic and Alpine Research (copyright by the Regents of the University of Colorado) and Scientific Reports (copyright by Nature Publishing Group). We thank Ole Wiggo Røstad for producing several figures, Oddvar Hanssen for photographing beetles, Steve J. Coulson for the photograph of C. foveolata and Valerie Behan-Pelletier for SEM photograph of Camisia horrida.
\n
\n',keywords:"succession, beetles, spiders, springtails, mites, glacier foreland, moisture, alternative successional pathways, geo-ecology",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/55038.pdf",chapterXML:"https://mts.intechopen.com/source/xml/55038.xml",downloadPdfUrl:"/chapter/pdf-download/55038",previewPdfUrl:"/chapter/pdf-preview/55038",totalDownloads:563,totalViews:227,totalCrossrefCites:1,totalDimensionsCites:3,hasAltmetrics:0,dateSubmitted:"October 19th 2016",dateReviewed:"February 28th 2017",datePrePublished:null,datePublished:"October 4th 2017",dateFinished:null,readingETA:"0",abstract:"Here, we explore 200 years of arthropod succession by using dated moraines in a Norwegian glacier foreland. Surface active beetles (Coleoptera) and spiders (Aranea) were sampled by pitfall trapping, and springtails (Collembola) and mites (Acari) were extracted from soil samples. Newly deglaciated ground was rapidly colonised by a mixture of generalists and specialists, with various life strategies. Interestingly, the pioneer community was fed by three ‘invisible’ food sources: biofilm with terrestrial diatom algae, tiny pioneer mosses and chironomid midges whose larvae were pond-living and used ancient carbon that was released by the melting glacier as an energy source. The true ‘super-pioneers’ were biofilm-eating springtails, which tracked the melting ice edge closely. Most species of beetles and springtails colonised within 80 years, while spiders and oribatid mites needed a longer time span to colonise. Topography influenced the succession pattern. Among both surface-living macroarthropods and soil-living microarthropods, we distinguished between a ‘dry’ and a ‘wet’ successional pathway with different community structure. Most arthropod species persisted after colonisation, but certain species preferring open space or low temperature were gradually excluded. Comparisons are made with botanical succession. Sampling methods, material size, and taxonomic resolution were considered critical factors when studying arthropod succession.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/55038",risUrl:"/chapter/ris/55038",book:{slug:"glacier-evolution-in-a-changing-world"},signatures:"Sigmund Hågvar, Mikael Ohlson and Daniel Flø",authors:[{id:"198836",title:"Prof.",name:"Sigmund",middleName:null,surname:"Hågvar",fullName:"Sigmund Hågvar",slug:"sigmund-hagvar",email:"sigmund.hagvar@nmbu.no",position:null,institution:{name:"Norwegian University of Life Sciences",institutionURL:null,country:{name:"Norway"}}},{id:"204828",title:"Prof.",name:"Mikael",middleName:null,surname:"Ohlson",fullName:"Mikael Ohlson",slug:"mikael-ohlson",email:"mikael.ohlson@nmbu.no",position:null,institution:null},{id:"204830",title:"Dr.",name:"Daniel",middleName:null,surname:"Flø",fullName:"Daniel Flø",slug:"daniel-flo",email:"daniel.flo@nibio.no",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Materials and methods",level:"1"},{id:"sec_2_2",title:"2.1. Study site",level:"2"},{id:"sec_3_2",title:"2.2. Microarthropod sampling",level:"2"},{id:"sec_4_2",title:"2.3. Macroarthropod sampling",level:"2"},{id:"sec_5_2",title:"2.4. Sticky traps and fallout traps",level:"2"},{id:"sec_6_2",title:"2.5. Gut content analyses",level:"2"},{id:"sec_8",title:"3. Succession patterns",level:"1"},{id:"sec_8_2",title:"3.1. Succession in species numbers",level:"2"},{id:"sec_9_2",title:"3.2. Succession in dominance structure",level:"2"},{id:"sec_10_2",title:"3.3. Do species persist after colonisation?",level:"2"},{id:"sec_11_2",title:"3.4. Relations to environmental parameters",level:"2"},{id:"sec_11_3",title:"3.4.1. Parameters related to age",level:"3"},{id:"sec_12_3",title:"3.4.2. A ‘wet’ and a ‘dry’ successional pathway",level:"3"},{id:"sec_14_2",title:"3.5. Succession of surface animals versus soil animals",level:"2"},{id:"sec_15_2",title:"3.6. Comparison between plant and animal succession",level:"2"},{id:"sec_16_2",title:"3.7. Pioneer arthropods—a heterogenic group",level:"2"},{id:"sec_18",title:"4. Dispersal: how to get there?",level:"1"},{id:"sec_19",title:"5. Food sources: how to survive?",level:"1"},{id:"sec_19_2",title:"5.1. Terrestrial biofilm as food",level:"2"},{id:"sec_20_2",title:"5.2. Pioneer mosses as food",level:"2"},{id:"sec_21_2",title:"5.3. Ancient carbon as food",level:"2"},{id:"sec_22_2",title:"5.4. A pioneer food web",level:"2"},{id:"sec_23_2",title:"5.5. Feeding categories during succession",level:"2"},{id:"sec_25",title:"6. Driving forces in various phases of animal succession: facilitating and inhibiting factors",level:"1"},{id:"sec_25_2",title:"6.1. Age 3–7 years: bare ground or only scattered pioneer vegetation",level:"2"},{id:"sec_26_2",title:"6.2. Age about 30–40 years: patchy pioneer vegetation and much open ground",level:"2"},{id:"sec_27_2",title:"6.3. Age about 60–200 years: mainly closed vegetation",level:"2"},{id:"sec_28_2",title:"6.4. Age about 10,000 years: mature soil",level:"2"},{id:"sec_30",title:"7. Remarks",level:"1"},{id:"sec_31",title:"8. Conclusions",level:"1"},{id:"sec_32",title:"Acknowledgments",level:"1"}],chapterReferences:[{id:"B1",body:'Oerlemans J. Extracting a climate signal from 169 glacier records. Science. 2005; 308: 675‐677.\n'},{id:"B2",body:'Jomelli V, Khodri M, Favier V, Brunstein D, Ledru M-P, Wagnon P, Blard P-H, Sicar, J-E, Braucher R, Grancher D, Bourlès DL, Braconnot P, Vuille M. Irregular tropical glacier retreat over the Holocene epoch driven by progressive warming. Nature. 2011; 474: 196‐199.\n'},{id:"B3",body:'Malcomb NL, Wiles GC. Tree-ring-based reconstructions of North American glacier mass balance through the Little Ice Age ‐ Contemporary warming transition. Quaternary Research. 2013; 79: 123‐137.\n'},{id:"B4",body:'Matthews JA. 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Department of Ecology and Natural Resource Management, Norwegian University of Life Sciences, Ås, Norway
The Norwegian Institute of Bioeconomy Research, Ås, Norway
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1. Introduction
The Monte Carlo (MC) method history began two centuries before its computational implementation that happened in the period of World War II (1939–1945). The MC method conception starts in 1733 with the Probléme de l’aiguille (Needle’s problem) by Georges-Louis Leclerc, known as the Comte de Buffon [1], which is enunciated as:
Sur un plancher qui n est formé que de planches égales & parallèles, on jette une Baguette d’une certaine longueur, & qu’on suppose sans largeur. Quand tombera-t-elle franchement íùr une seule planche? Leclerc [1], p. 44
or, translated to English:
On the floor formed only of equal boards placed in parallel, one throws a needle of a certain length which and supposed without width. When will this needle fall on one specific board?
The first solution proposed by Leclerc [2], in 1777, is considered one of the oldest geometrical probability solutions. The method basically consists in generating successive random samples N that will be tested in a statistical model representing the statistical probability. To use this method, one needs to satisfy the main condition: the random variable evaluated must be independent, which means that previous events of interest may not have (may have the minimum) an influence on the successive tryings. In the needle case, Leclerc ([2], pp. 100–104), presented a solution considering the distance D of the limits of each wood board and the length l of the needle (l < D) taking the probabilities of crossing zero lines and one line as [3].
p0=1−2rθandp1=2rθ,wherer=lDandθ=1π.
It seems to be a simple problem, but its solution ensued a sequence of different mathematical methodologies [3]. For example, in 1812, Laplace, using his theory of probability and theoretical calculations based on this methodology to determine an approximation to the π value [3, 4], presented a generalized solution in 3D space [3, 4, 5].
Following the main condition of independence for random variable enunciated by Leclerc [2], the MC method was proposed as an alternative solution to analytical mathematics to evaluate the behavior of random samples to predict a statistic sample distribution or a statistic behavior. This behavior can be assessed by empirical processes of drawing sequences of independent random samples and observing its behavior [6]. The strategy is simple in concept, but it is time-consuming, being the first computerized MC simulation developed and implemented by the working team of John and Klara von Neumann and Nick Metropolis with the advent of the computers in 1947–1948 [7].
There are different algorithms [8, 9, 10] implemented to apply different MC solutions by using different computational tools. Since the objective of this chapter is to present MC validation and/or reliability for application developers (AD), on a specific study case, we will not detail the different MC algorithms.
There are several characteristics that can be used to classify MC computational tools (MCCT); however, based on the objective of this chapter, the available ones will be classified according to its applicability as general and specific MCCTs. So, in section 2, the general concepts and MCCT code core (cross-sectional libraries and pseudorandom generators), including the specific and general MCCTs characteristics and some codes available nowadays, are going to be presented. In section 3, the validation and reliability of MCCT code concepts and main methods, including its limitations on the implementations of cross-sectional libraries and random generators, are going to be discussed. To illustrate this, a case study of validation for dosimetry in mammography using two MCCT methods for radiation transport (Geant4 and XRMC) is going to be presented. In the last section, the final considerations on choosing a MCCT and important issues on validation or reliability tests will be presented.
2. Monte Carlo general concepts and core
The MC method may be used to solve different kinds of problems. It may be used to solve problems that could also be solved by deterministic calculations, but it is usually more time-consuming than those and can increase the complexity of the solution. MC must to be applied, generally, when the change in the model follows a “time dependence” and is suitable for a stochastic calculation, which depends on a sequence of random numbers generated during the simulation. It means that a new execution of the solution with a new (different) sequence of random numbers for the same simulation will not give identical results. However, it will return values that agree with the results obtained from the previous sequence within some “statistical error” or in a statistical fluctuation range [11].
In a general manner, the problems that are in essence managed by random phenomena can be solved by applying MC [11, 12]. The main idea of MC method is to estimate a quantity, based on systems that use random numbers to simulate random walks [11], with an estimator computed from observed/experimental data [12]. Considering this idea, the core system of a MCCT is based on a randomized algorithm (random number generator) to manage probabilities (libraries of sampling distribution) [12]. A MCCT has other tools implemented, but for an AD, the knowledge of the MC core limitations is essential to estimate the accuracy and precision of the results.
Taking into account the proposition of MCCT for transport radiation, one may define core as the computational random number generator (randomized algorithm) and the cross sections for each possible process of interaction (probabilities, in the case of photons that can be the total attenuation cross sections for each possible process, or the differential cross sections—if applicable—or the energy transfer cross sections or the energy absorption cross sections). Let’s think about a traditional MC simulation as is represented in the following scheme (Figure 1). It is important to keep in mind that this is a simplified scheme of transport radiation designed to aid the understanding of the basics of MC processing. Before one starts to run1 an event2 in a MCCT, one may define the simulation universe (or world), including the geometry, material composition of the simulated objects, and, if necessary, the additional information needed for the interaction.
Figure 1.
Simplified scheme of a traditional Monte Carlo simulation.
The run starts always with the generation of a primary particle (emitted by the radiation source), and it finishes when all histories were run. As one may observe in Figure 1, the system starts the run, after the geometry built and physics definition, by initializing the counter of the number of histories (VARnh). This variable is compared to the expected total number of histories (nh), so if the VARnh is equal to nh, then the termination of run is performed, or if VARnh is smaller than nh, then a new history is started by generating a new primary particle. In the generation of primary particle, if the source is defined by an energy distribution and/or position distribution (linear, planar, or volumetric source) and/or momentum direction distribution, the random number generator will be evoked (one to each distribution needed). After the primary particle of the source is generated, the information about this particle is recorded at the beginning of step (pre-step information). Following the step execution, the end of the step information will be generated (post-step information) and tested. The traditional MCCT tests are:
Is this particle inside the world? In MC simulation, the geometrical limits to follow the transport of radiation are the limits described on the geometry by the larger volume (the world) that will contain the other volumes. Some MCCTs have no world volume defined; usually if they are specific MC using variance reduction techniques that force the radiation to interact with the defined volumes, then the logic is different than the presented in the scheme in Figure 1.
Is this particle alive? In MCCT for transport radiation, there is a minimum energy to proceed the transportation, so if the particle kinetic energy is smaller than this minimum energy, then this particle will die, which means in MCCT all residual energy will be locally deposited and the particle will stop.
If the particle is alive and inside the world, then it is important to know if this particle will find a geometrical boundary and/or a different material in its path during the step. If the answer is no to both pre-defined questions, then the code will proceed with the step. If the answer is yes, the code will calculate the length until this boundary and check if the other volume has or does not have a new material, and the step will proceed until the boundary; after that the residual kinetic energy of the particle will be recalculated for the next volume material. At the end of the step, the post-step information is recorded. Then, the VARnh is increased of a unit and is compared to nh. If VARnh is equal to nh, the termination of run is performed. If VARnh is smaller than nh, a new step procedure is started by recording the post-step information of the previous step as initial information of the new one, proceeding with the verifications and implementations for this new step. It is important to note that all secondary particles generated, as product of an interaction, will be transported following the same procedure starting in Record Pré-Step with the exception that VARnh will not be incremented and these particles will be followed until they die or leave the world.
To illustrate the selection of random number, let’s create a hypothesis of a 40 keV photon interacting with a liquid water medium. In this case, the total attenuation cross section is 0.2683 cm2/g, being composed by coherent scattering (0.02874 cm2/g), incoherent scattering (0.1827 cm2/g), and photoelectric effect (0.05680 cm2/g).3Figure 2 shows the simplified scheme that defines the process of interaction.
Figure 2.
Scheme of the random generator logic to define a probability of interaction of a 40 keV photon into liquid water medium.
Considering the information in Figure 2, one may see that among the three possible processes of interaction a probability of approximately 10.71% for coherent scattering, 68.11% for incoherent scattering, and 21.18% for photoelectric effect. Then, the normalization of the probabilities for each process between 0 and 1 is performed, considering the total attenuation cross section as the normalizing factor, and these normalized probabilities are organized in a sequence of real values. The possible number of values between 0 and 1 depends on the variable type defined in the MCCT implementation for the random generator number. On the presented example, the random numbers in the intervals [0; 0.10714) identify coherent scattering [0.10714; 0.78824), incoherent scattering, and [0.78824; 1) photoelectric effect. It is important to note that the probability of occurrence is proportional to the quantity of random numbers in the sequence of values. In the case exemplified in Figure 2, the number 0.0053721 is in the range [0; 0.10714) and defines the photon transport by coherent effect. If the random number were 0.78824, the photoelectric effect would be simulated since this value is in the range [0.78824; 1).
During the simulation several processes may need to have a random number generated such as process of interaction (used in the above example), momentum direction of the particle, secondary particle momentum and kinetic energy, atomic effect (if considered in the simulation), probability of Auger effect, and momentum direction of the Auger electron or auto-absorption of the Auger electron, among others. After the random definition of some of the abovementioned characteristics, deterministic equations are applied to keep the Principle of Energy and Momentum Conservation. Regarding the core of MCCT, it is important to know, as an AD, the main validity and limitations of the random number generator and the cross-sectional libraries.
The random number generator may be classified as pseudorandom number generator (PRNG) or true random number generator (TRNG) [13]. The so-called PRNG uses a deterministic process to generate a series of outputs from an initial seed state which means that for the same input “seed” one may have the same output number [13, 14, 15]. As an example one may cite the <cstdlib> head of C++ rand() function. In this case, usually the random number generated is an integer, and to know the range of possible numbers, it helps the AD to understand the limitations of the number of histories that can be run without compromising the randomicity of the simulation [13, 14], the so-called period of random number generator [16]. Table 1 presents the different range of values generated among the possible integer variables according to [14].
Type
Storage size
Values range
Short
2 bytes
−32,768 to 32,767
Int
2 bytes or 4 bytes
−32,768 to 32,767 or −2,147,483,648 to 2,147,483,647
Long
4 bytes
−2,147,483,648 to 2,147,483,647
Unsigned short
2 bytes
0 to 65,535
Unsigned integer
2 bytes or 4 bytes
0 to 65,535 or 0 to 4,294,967,295
Unsigned long
4 bytes
0 to 4,294,967,295
Table 1.
Type of integers, storage size, and range of possible values in C++ programming language.
Based on the value range presented in Table 1, one may see that different possible variable definitions of the random generator can affect the resolution of the simulation, which means that there is a limit of histories with a proper random behavior for a PRNG. The PRNG is used in several applications [15], and one advantage of using it on MCCT is the capability of reproducing the same sequence of pseudorandom numbers [14] that can be used to validate an application and/or to validate and test different installations of a MCCT under different environments (evaluating the accuracy and precision of the simulation in different conditions) [16].
The TRNG uses a non-deterministic source to produce randomness [13], and its advantage is that TRNG is unpredictable, unbiased, and independent [16]. The disadvantage on developing TRNG is that it is implemented in hardware, which limits the flexibility of this random number generator and since additional verification of randomness is required with every change of environment [16]. Because of the hardware implementation of TRNG, computers without a hardware random number generator will require a peripheral that will generate a TRNG seed to be used as incoming data for PRNG [16].
Sometimes, an association of random number generators (PRNG-PRNG and PRNG-TRNG) is implemented to increase the period of a random generator, but the randomness of the number generated must be tested and verified. Special care must to be taken attention on running MCCT in computational grids or clusters to ensure that every processor will have an independent random seed to start the process. If this requirement is not kept, inconsistencies in the results may happen turning them unrealistic and carrying with them statistical tendencies that do not represent the expected probabilities. Therefore, to guarantee the reliability of results of a MCCT, the AD must understand the random number generator and its period and limitations.
Considering the reliability of the MCCT in the example described above, when it is applied to low-energy radiation transport, the probabilities (e.g., cross-sectional libraries—total and differential—for photons), the distribution functions, and the transport models for particles, such as electrons, are indispensable. As a general rule, it is important to know the processes simulated and if there are one or more models to be evoked. To validate these characteristics, the MCCT requires a microscopic validation4 that in turn requires experimental data of the cross sections or distribution functions for different material and energy range. The microscopic validation is hard work to be performed by an AD; however, one may find the validation of the data libraries in the literature and/or online libraries [17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27] and on independent validations published for specific MC codes [21, 28, 29, 30].
2.1 General versus specific Monte Carlo toolkit for radiation transport
The MCCT may be classified according to its applicability as general purpose (GP) [31, 32, 33] or specific purpose (SP) [33, 34, 35]. It is important to understand that this classification refers to the possibility of using MCCT in different applications and not the kind of solution generated by the MCCT. All MCCTs present a general solution to the study case, when applied to the same particle types, degrees of freedom, and simulated quantities, taking into account the limitations of the implemented code and libraries.
Some MCCTs are developed considering the simulation of a wide range of particles and/or quantities. Usually these MCCTs simulate detailedly the radiation transport of primary and secondary particles using minimal approximations as possible. These MCCTs are called general purpose Monte Carlo toolkit (GPMCT), and they may be applied to solve a wide range of radiation transport problems: large energy range, different particle types, different geometries, and a large range of simulated processes. As examples, one may cite Geant4, MCNP, or FLUKA.
The geometry and tracking (Geant4) [36, 37, 38] is a MCCT that has a complete range of functionalities including tracking, geometry, physics models, and hits [36]. It was developed based on object-oriented technology and implemented in C++ programming language. The physics processes available cover a comprehensive range, including electromagnetic, hadronic, and optical ones with a large set of materials, chemical elements, and long-lived particles, over a wide energy range starting from 250 or 990 eV and extending to a few TeV. The extended package Geant4-DNA adds processes for the modeling of induced biological damage by ionizing radiation at DNA scale, which transports all particles using a discrete model [39, 40, 41, 42] extending the possibility of transport particles down to a few eV (the range is different to each particle and process). On Geant4, the AD may access a large cross-sectional library database, making possible to choose different radiation processes and, to each process, to select different transport models. On Geant4, the AD may implement different variance reduction methods and set different parameters to transport primary and secondary particles [43] among the more than 35 particles5 allowed [43]. AD may use Geant4 classes to create collections of interactions, named hits (G4VHit or G4THitsCollection), and/or evoke sensitive detector counters (G4MultiFunctionalDetector or G4VPrimitiveScorer) and/or implement his/her own personal class (a new sensitive detector or hit file) [44].
The Monte Carlo N-particle (MCNP6) [45, 46, 47, 48, 49] MCCT includes a powerful general source, a criticality source, and a surface source. In addition to that, this MCCT includes both geometry and output counter (named tally) plotters. MCNP is implemented on GNU Fortran and C/C++ compilers [49] being a continuous-energy, generalized-geometry, time-dependent, MC radiation-transport code designed to track many particle types over broad ranges of energies. This MCCT may simulate neutron, photon, electron, or coupled neutron/photon/electron transport and heavy ions [49]. It simulates different energy ranges for different particles: neutron energy range from 10−11 to 20 MeV for most of isotopes and up to 150 MeV for some others, photon energy range from 1 keV up to 100 GeV, and electron energy range from 1 keV to 1 GeV [50]. It has a rich collection of variance reduction techniques with an extensive collection of cross-sectional data. In addition, MCNP contains numerous tallies: surface current and flux, volume flux (track length), point or ring detectors, particle heating, fission heating, pulse height tally for energy or charge deposition, mesh tallies, and radiography tallies [46, 49]. This MCCT makes it possible to change transport parameters by command lines [46, 50].
The Fluktuierende Kaskade (FLUKA) [51, 52, 53] MCCT was implemented and presents a number of ADs interface routines in Fortran 77. It simulates accurately the interaction and propagation of radiation in matter of about 60 different particles,6 including photons and electrons from 100 eV or 1 keV to thousands of TeV, neutrinos, muons of any energy, hadrons of energies up to 20 TeV and all the corresponding antiparticles, neutrons down to thermal energies, and heavy ions. Efficiency on radiation transport has been achieved using a frequent access table look-up sampling, and accuracy is maximized by systematic use of double precision variables. It is provided with a large number of available options for an AD and has been completely restructured introducing dynamical dimensioning. It has the double capability to be used in a biased mode as well as a fully analogue code which means that while it can be used to predict fluctuations, signal coincidences, and other correlated events, a wide choice of statistical techniques is also available to investigate punch through or other rare events in connection with attenuations by many orders of magnitude [52]. FLUKA can generate several output cards: a main (standard) output file, two scratch files, a file with the last random number seeds, an error messages file (if any), and any number (including zero) of estimator output files. Generally, the AD may choose between formatted and unformatted output and may generate a personalized routine for additional outputs [53].
However, some MCCTs are developed to solve problems considering specific particles or specific geometrical conditions or specific simulated quantities. These MCCTs are called specific purpose Monte Carlo toolkit (SPMCT) and are usually optimized to use several approximations and variance reduction techniques. They are developed considering restrictions on applications, and very specific quantities are simulated. In general, the SPMCTs are faster than the GPMCTs to solve the same problem. As examples, one may cite XRMC, ITS TIGER series, PENELOPE, EGS, and ETRAN.
The X-Ray Monte Carlo (XRMC) [54] simulates accurately X-ray imaging and spectroscopy experiments of heterogeneous samples. This MCCT is implemented in C++ and is capable of simulating, in detail, complex experiments on generic samples using different variance reduction techniques by default. It was developed initially to simulate X-ray fluorescence and photon imaging. XRMC simulates the transport of photons only and makes it possible to simulate the following quantities: total fluence and fluence with energy binding and total energy fluence and energy fluence with energy binning. As output, it may generate a raw file with the transmission image [55], and if energy binning is evoked, the AD may define the bin size. On transport possibilities, the AD may define maximum scattering order number, maximum scattering order as transmission, first-order scattering or fluorescence emission, and second-order scattering or fluorescence emission or higher order. It also has the flexibility of activating or inactivating fluorescence [54, 55] process. The cross-sectional library evoked by XRMC is the xraylib [56], a library for X-ray matter interactions generally used for XRF applications.
The integrated tiger series (ITS) [57, 58, 59], version 6, allows solutions of linear time-independent coupled electron/photon radiation transport problems. This MCCT employs accurate cross sections, sampling distributions, and physical models to describe the production and transport of the electron/photon cascade from 1.0 keV to 1.0 GeV [58, 59]. The ITS, version 6, was converted to Fortran 90 [59] with C++ links to CAD software. The availability of the source code allows the AD to tailor this MCCT to specific applications and to extend its capabilities to more complex applications. Overlaps in CAD geometry may be evaluated and reported in an output file [58]. The AD may set different parameters by command line like to define the cross section for different data sets, to deactivate the coherent photon scattering, to include (or not) binding effects in incoherent photon scattering, and/or to apply (or not) energy-loss straggling to electrons [59]. The AD may set different output information such as the energy and charge deposited in every subzone, the detailed energy and charge deposited in every subzone, and the geometry-dependent input settings [58]. ITS’ cross-sectional [58] suite of codes includes a multigroup version along with the multigroup cross-sectional generator CEPXS and a continuous-energy (XGEN) cross sections [58, 59]. In ITS, photons below 1 keV are locally absorbed, an alternative algorithm to electron transport was implemented named Generalized Boltzmann Fokker-Planck (GBFP), and the full transport capability for photons and electrons using the Livermore database is under development [58].
The penetration and energy loss of positrons and electron (PENELOPE) [60], version 2014, MCCT simulates the coupled electron-photon transport as well as photons, electrons, and positrons. The PENELOPE simulation algorithm is based on a scattering model combining numerical databases with analytical cross-sectional models for the different interaction mechanisms being applicable to energies from few hundred eV up to approximately 1 GeV. Photon transport is simulated by means of the standard, detailed simulation method. Electron and positron transports are simulated based on a mixed procedure, which combines a detailed simulation with a condensed one [60, 61, 62, 63]. The implementation of the cross-sectional libraries considers EPDL7 total cross sections for photoelectric absorption and Rayleigh scattering, XCOM8 cross sections for pair production, and SUMGA9 function for total atomic cross sections and Compton scattering. PENELOPE can simulate the emission of characteristic X-rays and Auger electrons resulting from vacancies produced in K, L, M, and N shells by photoelectric absorption, Compton scattering, triplet production, and electron/positron impact. In PENELOPE 2014, the elastic collisions of electrons and positrons are simulated, using numerical partial-wave cross sections for free neutral atoms by elastic scattering of electrons and positrons by atoms (ELSEPA) program that is a database distributed by ICRU Report 77 (2007) [60]. The output may be defined using Fortran subroutines, where the AD may get different quantities such as number of materials that were loaded, mass density of specific materials, characteristics of the slowing down for charged particles, energy of the particle at the beginning of the track segment, effective stopping power of soft energy-loss interactions, and energy lost along the step, among others [61].
The electron gamma shower (EGS) MCCT may be found on different main versions, EGS5 and EGSnrc. Both versions of EGS are implemented in Mortran3 language, which is a preprocessor for Fortran [64, 65]. The origins of EGS MCCT are documented in NRC-PIRS-0436 report [66]. The EGS5 simulates the coupled transport of electrons and photons in an arbitrary geometry for particles with energies from a few keV up to a several hundred GeV [64] depending on the atomic numbers of the target materials. The EGSnrc10 (Electron Gamma Shower from National Research Council) is an extended and improved version of the EGS MCCT, having specific modeling implementations to electron and photon transport through matter. It includes the BEAMnrc software component that models beams traveling through consecutive material components, ranging from a simple slab to the full treatment head of a radiotherapy linear particle accelerator (linac). EGSnrc is particularly well-suited for medical physics applications (research and devices development) being used for medical radiation detection, medical image based on x-radiation, and dosimetry for a specific volume. However, due to the flexibility of this MCCT, the AD may use it for different applications such as in industrial linac beams, X-ray emitters, radiation shielding, and more. The EGSnrc simulates the radiation transport in homogeneous materials for photons, electrons, and positrons with energies between 1 keV and 10 GeV. It incorporates significant refinements in charged particle transport and better low energy cross sections and makes it possible to define elaborated geometries and particle sources [65].
The electron transport (ETRAN) MCCT transports electrons and photons through extended media being developed by the National Bureau of Standards. This MCCT has various versions representing mainly refinements, embellishments, and different geometrical treatments that share the same basic simulation algorithm based on random sampling the path of electrons and photons as they travel through matter. The algorithms and computational tools written at other laboratories, such as Sandia’s older SANDYL code and their more current series of the TIGER, CYLTRAN, and ACCEPT codes, together have been called ETRAN model too.
When an AD chooses a MCCT, it is important to consider:
The characteristics of the application: type of primary and secondary particles and their energy range, quantities to be simulated, geometry and material composition of the simulated universe;
The capabilities of the MC code: if the code can handle properly the transport of primary and (if necessary) secondary particles in the energy range of interest, if it is possible to simulate the necessary quantities, and if it can handle the transport simulation in all material compositions expected and how it simulates the geometry of interest;
The limitations of the MC code: transport processes and models simulated in the energy range of interest (search for microscopic validation of the cross-sectional libraries published) and how accurate the MCCT is on simulating the dosimetric quantities and the particle fluxes (search for macroscopic validation published), being recommended that the AD proceeds his/her own macroscopic validation;
The computational performance: verifying the running time to get an acceptable statistical fluctuation in the results for the cases of interest and, in some cases, checking the RAM memory used to build the virtual universe and the memory used to save the output files;
Considering those minimal guidelines on choosing a MCCT, there is a good chance for the AD to not have unresolvable problems during the development of an application. Now, if you, as an AD, still have questions about the proper MCCT to choose, keep in mind the best one is the MCCT able to solve your “problem” (accuracy of the results) with an adequate statistical fluctuation (precision of the results). In addition to that, an AD at least should be able to install and to use the MCCT interface, being aware of the common limitation of it. All these characteristics may be found, usually, in the manual (user manual and physics process manual).
3. Verification, validation, comparison, and reliability of Monte Carlo toolkits
To guarantee that one application is realistic, it is important to test it (computational code) in different ways. There are several known ways to test a computational code and its parts; however, in this section, the focus is to present the concepts applied on developed applications for MCCTs such as verification, validation, comparison, and reliability.
When one is working in an application for MCCT, it is important to understand the concepts that may guarantee its internal consistency and accuracy. The IEEE 1012–2016 gives a general description of software verification and validation, and the IEEE 24765–2017 gives a detailed description of these concepts defining these terms. Verification is defined as a “confirmation by examination and provisions of objective evidence that specified requirements have been fulfilled” (IEEE 1012–2016), and lately this concept was detailed as “the process of evaluating a system or component to determine whether the products of a given development phase satisfy the conditions imposed at the start of that phase” (IEEE 24765–2017). Validation is defined as a “confirmation by examination and provisions of objective evidence that the particular requirements for a specific intended use are fulfilled” (IEEE 1012–2016), and lately this concept was detailed as “the process of evaluating a system or component during or at the end of the development process to determine whether it satisfies specified requirements” (IEEE 24765–2017). So, one may say that a validation was performed when this one answers affirmatively the question: “Are we building the right product?” In the other hand, one may affirm that one is doing a verification by answering the question: “Are we building the product right?” [67].
According to [68], “Validation involves the system and acceptance testing during the test phase, whereas verification involves reviews and audits, software unit testing, and other techniques to evaluate intermediate work products such as the software requirements specification, software design description, and individual modules during earlier project phases.” In MC, the AD does the verification of the application developed to guarantee that this application is reproducing the system (or geometry) and general conditions as close as possible to the reality, and the AD does the validation to guarantee that the MC application (considering the geometry material, particles if interaction and energy range of the particles) gives realistic results when compared statistically to experimental data, when a consistent amount of quantitative experimental data is available. In this context, it is fundamental to understand the setup and the experimental limitations of the instruments and measurements used in the experiments to take it into account on the data analyses to explain observed differences and similarities on the results.
When experimental data is not available, it is possible to use other MCCT or deterministic models to compare to the MC application results. In this way, one is performing a comparison between models and not a validation. This comparison must be based on quantitative statistical tests. In this case, to know and understand the main conceptions involved in the models and databases used, including its limitations and previous validations, it is fundamental to explain the observed differences and similarities on the results.
A reliability evaluation is recommendable when there are neither experimental data on specific trustable models nor amount of data to perform a validation or a comparison. The IEEE 982.1–2005 provides information used as indicators of reliability defining software reliability as “the probability that software does not cause the failure of a system for a specified time under specified conditions.” In this context, the software reliability represents an effective measurement of the more general concept of software quality, using derived quantities and experimental models that are partially consistent to the application of interest. It is important to know the systematic errors and map all differences on the contour limitations of the application and the theory involved in this comparison.
It is possible to combine validation results, comparison between models, and software reliability to evaluate an application. Additional information about statistical tests and specific recommendations for software verification, validation, reliability, and comparison may be found in international documents. Thus, it is important to study the international standard regulations/recommendations when one wants to validate any software, including the MCCTs themselves and applications developed using them. The standard lists of active documents from IEEE, International Electrotechnical Commission (IEC), and International Organization for Standardization (ISO) may be searched online.11 Additional detailed information about this subject may be studied at:
IEEE 730–2014—IEEE Standard for Software Quality Assurance Processes
IEEE 982.1–2005—IEEE Standard Dictionary of Measures of the Software Aspects of Dependability
IEEE 1012–2016—IEEE Standard for System, Software, and Hardware Verification and Validation (corrigendum 1012–2016/Cor 1–2017)
IEEE 1016–2009—IEEE Standard for Information Technology-Systems Design—Software Design Descriptions
IEEE 12207–2017—ISO/IEC/IEEE International Standard—Systems and software engineering—Software life cycle processes
IEEE 14764–2006—ISO/IEC/IEEE International Standard for Software Engineering—Software Life Cycle Processes—Maintenance
IEEE 15026–1—Revision-2019—ISO/IEC/IEEE Approved Draft International Standard—Systems and Software Engineering—Systems and Software Assurance—Part 1: Concepts and Vocabulary
IEEE 15026–2-2011—IEEE Standard—Adoption of ISO/IEC 15026–2:2011 Systems and Software Engineering—Systems and Software Assurance—Part 2: Assurance Case
IEEE 15026–3-2013—IEEE Standard Adoption of ISO/IEC 15026–3—Systems and Software Engineering—Systems and Software Assurance—Part 3: System Integrity Levels
IEEE 15026–4-2013—IEEE Standard Adoption of ISO/IEC 15026–4—Systems and Software Engineering—Systems and Software Assurance—Part 4: Assurance in the Life Cycle
IEEE 24765–2017—ISO/IEC/IEEE International Standard—Systems and software engineering—Vocabulary
IEEE 29119–1-2013—ISO/IEC/IEEE International Standard—Software and systems engineering—Software testing—Part 1: Concepts and definitions
IEEE 29119–2-2013—ISO/IEC/IEEE International Standard—Software and systems engineering—Software testing—Part 2: Test processes
IEEE 29119–3-2013—ISO/IEC/IEEE International Standard—Software and systems engineering—Software testing—Part 3: Test documentation
IEEE 29119–4-2015—ISO/IEC/IEEE International Standard—Software and systems engineering—Software testing—Part 4: Test techniques
IEEE 29119–5-2016—ISO/IEC/IEEE International Standard—Software and systems engineering—Software testing—Part 5: Keyword-Driven Testing
IEC 61508–4 (2010–2104)—Functional safety of electrical/electronic/ programmable electronic safety-related systems—Part 4: Definitions and abbreviations
IEC 61508–5 (2010–2104)—Functional safety of electrical/electronic/programmable electronic safety-related systems—Part 5: Examples of methods for the determination of safety integrity levels
IEC 61508–6 (2010–2104)—Functional safety of electrical/electronic/programmable electronic safety-related systems—Part 6: Guidelines on the application of IEC 61508–2 and IEC 61508–3
IEC 61508–7 (2010–2104)—Functional safety of electrical/electronic/programmable electronic safety-related systems—Part 7: Overview of techniques and measures
IEC 61511–1 (2003–2101)—Functional safety—Safety instrumented systems for the process industry sector—Part 1: Framework, definitions, system, hardware and software requirements
IEC 61511–2 (2003–2007)—Functional safety—Safety instrumented systems for the process industry sector—Part 2: Guidelines for the application of IEC 61511–1
IEC 61511–3 (2003–2003)—Functional safety—Safety instrumented systems for the process industry sector—Part 3: Guidance for the determination of the required safety integrity levels
ISO/IEC 25010:2011—Systems and software engineering—Systems and software Quality Requirements and Evaluation (SQuaRE)—System and software quality models
There are two ISO documents under development at the moment: the ISO/DTR 11462–3 Guidelines for implementation of statistical process control (SPC)—Part 3: Reference data sets for SPC software validation and ISO/NP TR 11462–4 Guidelines for implementation of statistical process control (SPC)—Part 4: Reference data sets for measurement process analysis software validation.
3.1 Example of application for macroscopic validation, comparison, and reliability for XRMC and Geant4
On this section a comparison between XRMC version 6.5.0-2 (henceforth called XRMC) [54, 55] and Geant4 version 10.02.p02 (henceforth called Geant4) [36, 37, 38] is presented, as well as the validation of both MCCTs using experimental data collected on three different mammographs. For validation the following measurements were performed: exposure (X), kerma, half-value layer (HVL), inverse square law (ISL), and backscattering (BS). Limitations, advantages, and disadvantages of using a general and specific MCCT will be commented too. Absolute and normalized quantities were selected because it is important to know the correction factor for total number of photons generated per mAs per total irradiated area for each equipment (this number is characteristic of each X-ray tube and will change with the time), and the combination of these quantities helps to define the best approximation for this correction factor in the simulation to get results closer to the clinical reality.
It is important to inform that each setup had the data collected with calibrated equipment (electrometers and ionizing chambers) available at their institutions and performed by the same person that developed the application with both MCCTs. The simulated geometries are the same used on the data collection. In the following, a brief description of the measurement equipment and simulated setup is presented:
Mammomat Inspiration [69, 70] (henceforth called Inspiration)—measurements were performed with electrometer and ionizing chamber TNT 12000 kit (Fluke) and Al 99% purity filters. SIMULATION: dry air-sensitive volume of 15 cm3; focal spot as point-source irradiating homogeneously on circular surface of 2.08 cm of radius; spectra for acceleration voltages 25, 30, and 35 kVp; track-additional filtration combination Mo-Mo (30 μm) and Mo-Rh (25 μm); spectra of ripple 0%; target tilt angle of 20o; and a window of 0.8 mm of beryllium (Be). The HVL calculations are based on a source-to-detector distance of 41.0 cm for different Al thickness filtration; and X data were collected and simulated to source-to-detector distances 26, 40, 50, and 60 cm.
Mammomat 3000 [71] (henceforth called M3000)—measurements were performed with electrometer Victoreen model 660–1 (1315REV) and ionizing chamber Victoreen model 660-4A (512REV). SIMULATION: dry air-sensitive volume of 4 cm3; focal spot as point-source irradiating homogeneously on a circular surface of 10.0 cm2; spectra of ripple 0%; target tilt angle of 22o; a Be window 0.8 mm thick; track-additional filtration combinations of Mo-Mo (30 μm), Mo-Rh (25 μm), and W-Rh (50 μm); and spectrum acceleration voltages of 24 up to 32 kVp, in steps of 2 kVp. The BS was calculated considering simulators of BR12 epoxy and polymethilmetacrilate, considering a source-to-detector distance of 60.0 cm and simulator thicknesses of 4, 5, 6, and 8 cm.
Lorad MIII [72] (henceforth called Lorad)—measurements were performed with electrometer Modified Keitlhy (model 602) and ionizing chamber for mammography MPT SN 442. SIMUALTION: dry air-sensitive volume of 6.0 cm3; focal spot as point-source irradiating homogeneously on a rectangular surface of (18.0 × 24.0) cm2; spectra for acceleration voltages from 26 to 34 kVp, in steps of 2 kVp; track-additional filtration combination of Mo-Mo (30 μm) and Mo-Rh (25 μm); spectra of ripple 0%; target tilt angulation of 16o; and a Be window 0.8 mm thick. The X measurements were performed with compression paddle and by minimizing the BS effects by increasing the distance between the bucky and the ionizing chamber.
It is important to evaluate all the available possibilities on the MCCT to get a realistic perspective of the configurations. Because of that, two modes to describe the transport model were evaluated on XRMC (transmission (T) and with scattering for dosimetry (D)). In Geant4, the different radiation transport physics models recommended for low energy photons and electrons (standard-option3 (std), penelope (pen), and Livermore (liv)) were also evaluated. Since measurements of the experimental spectra were not possible, different descriptions of the incident spectra modeled by two different references [73, 74] were explored. When nonexperimental spectra are used to simulate dosimetric quantities, it is necessary to take into account the validation of normalized quantities and, if possible, to use semiempirical correction factors to get accurate values for the average number of photons per mAs per total irradiated area. There are different ways on doing it, but the usual are:
to use the ratio of the simulated and experimental KERMA to get a correction factor, generally using primary beam with different kVp and mAs, in the range of energy of interest, collecting the KERMA with the minimization of scattering effects or
to use a normalized quantity, for example, normalized HVL, to evaluate the proximity of the behavior of the simulated and experimental curves and then use a good of fit (GoF) test on the non-normalized HVL to estimate the best correction factor to fit the amplitude of the simulated to the experimental data.
In both cases, the error estimation of the experimental data as well as the quantification of the statistical fluctuations of the MC method must be taken into account.
The XRMC does not return the absorbed energy or dose as an output information, so to make the comparison of quantities calculated in same conditions possible, the calculations are based on the incoming spectra on the surface of the sensitive volume. The Geant4 application was planned to collect the spectra on the surface of the sensitive volume, and the same calculations applied to XRMC results were used. On the other hand, for Geant4 validation, the absorbed energy in the sensitive volume was used. The statistical fluctuations were based in a sequence of 10 runs with different seeds for each evaluated case, for both MCCTs, and the average and standard deviation of the data were calculated and used on data analyses.
It is important to compare quantitatively experimental to simulated data for validation. Several statistical tests usually may be applied generally: Chi-square (χ2), Anderson-Darling, Kolmogorov-Smirnov, and Walt-Wolfowitz, among others. However, when one has data with error or statistical fluctuation associated, the χ2 must be applied since it considers this in the nonparametric evaluation between the statistical populations of interest. Another simple way to start an evaluation of the results is to generate comparative plots. Figure 3 presents the graphical comparison of MCCT validations, and Tables 2 and 3 present the χ2p value for the validation and the comparison for all simulated conditions and normalized data.
Figure 3.
Relative difference between simulated and experimental data considering normalized data, with outliers, for different modeled spectra and all studied mammographs: Inspiration (a), M3000 (b), Lorad (c), and all equipment (d).
Transport models and spectrum identification
Inspiration (HVL)
M3000 (BS)
Lorad (HVL)
All
M3000 (Mo30Mo)
M3000 (Mo25Rh)
M3000 (W-25Rh)
Lorad (Mo30Mo)
Lorad (Mo25Rh)
XRMC_T–Barnes
0.3025
NA
1.0000
0.9988
NA
NA
NA
1.0000
0.7265
XRMC_T–Catalogue
0.0687
NA
0.5859
0.3125
NA
NA
NA
0.9258
0.1466
XRMC_D–Barnes
NA
1.0000
NA
1.0000
1.0000
1.0000
1.0000
NA
NA
XRMC_D–Catalogue
NA
1.0000
NA
1.0000
1.0000
1.0000
1.0000
NA
NA
G4std–Barnes
0.2463
1.0000
0.0817
1.0000
1.0000
1.0000
1.0000
0.9998
<0.001
G4std–Barnes–Calc
0.1966
1.0000
0.0785
1.0000
1.0000
1.0000
1.0000
0.1049
0.2069
G4std–Catalogue
0.1481
1.0000
<0.001
0.3636
1.0000
1.0000
1.0000
<0.001
<0.001
G4std–Catalogue–Calc
0.0710
1.0000
<0.001
0.9993
1.0000
1.0000
1.0000
0.1811
<0.001
G4pen–Barnes
0.2397
1.0000
0.1113
1.0000
1.0000
1.0000
1.0000
0.9999
<0.001
G4pen–Barnes–Calc
0.1564
1.0000
0.7587
1.0000
1.0000
1.0000
1.0000
0.9997
0.0597
G4pen–Catalogue
0.3511
1.0000
<0.001
0.3811
1.0000
1.0000
1.0000
<0.001
<0.001
G4pen–Catalogue–Calc
0.2383
1.0000
0.0102
1.0000
1.0000
1.0000
1.0000
0.3842
0.0018
G4liv–Barnes
0.2494
1.0000
0.9703
1.0000
1.0000
1.0000
1.0000
1.0000
0.2405
G4liv–Barnes–Calc
0.3756
1.0000
0.0600
1.0000
1.0000
1.0000
1.0000
0.0328
0.3994
G4liv–Catalogue
0.1910
1.0000
0.0290
1.0000
1.0000
1.0000
1.0000
<0.001
0.9905
G4liv–Catalogue-Calc
0.0331
1.0000
0.6826
1.0000
1.0000
1.0000
1.0000
0.1454
0.9993
Table 2.
χ2 p values for the validation for both MCCTs considering normalized quantities for all studied cases.
Transport models and spectrum identification
Inspiration (HVL)
M3000 (BS)
Lorad (HVL)
All
M3000 (Mo30Mo)
M3000 (Mo25Rh)
M3000 (W-25Rh)
Lorad (Mo30Mo)
Lorad (Mo25Rh)
G4std–Barnes
0.9777
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
0.9999
G4std–Barnes–Calc
0.9149
1.0000
0.9671
1.0000
1.0000
1.0000
1.0000
0.6334
0.9972
G4std–Catalogue
0.2139
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
0.9808
1.0000
G4std–Catalogue–Calc
0.1595
1.0000
0.9975
1.0000
1.0000
1.0000
1.0000
0.9200
0.9974
G4pen–Barnes
0.8606
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
0.9999
G4pen–Barnes–Calc
0.7994
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
0.9637
G4pen–Catalogue
0.1660
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
G4pen–Catalogue–Calc
0.1572
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
0.9997
G4liv–Barnes
0.9767
1.0000
1.0000
0.9998
1.0000
1.0000
1.0000
1.0000
1.0000
G4liv–Barnes–Calc
0.6809
1.0000
1.0000
0.9998
1.0000
1.0000
1.0000
0.4828
1.0000
G4liv–Catalogue
0.7014
1.0000
<0.001
0.9965
1.0000
1.0000
1.0000
1.0000
<0.001
G4liv–Catalogue–Calc
0.6993
1.0000
0.1663
1.0000
1.0000
1.0000
1.0000
1.0000
1.000
Table 3.
χ2 p values for the comparison between XRMC and Geant4 (references) considering normalized quantities for all studied cases.
The graphics in Figure 3 present a visual interesting result for the evaluation of the relative difference between experimental and simulated data taking experimental data as reference. It shows that different systems may be better represented by different modeled spectra. The Inspiration setup (Figure 3a) shows similar results for both modeled spectra since all relative differences for median, first and third quartiles, are between −10 and −2%. A small number of outlier data are observed in this case. The M3000 (Figure 3b) evaluation clearly presents better accuracy and precision using spectra from Barnes et al. [74], since it presents all median data closer to 0% and the lowest data dispersion among the three mammographs represented by smallest first and third quartiles (in the range of −3 and 2%). For Lorad (Figure 3c) a better accuracy of the results is visible when spectrum from Barnes et al. [74] is used specially with Geant4, because all data for these spectra presented median closer to 0% and the data for catalogued spectra [73] presented medians between −6 and −3%. However, for this mammograph, there is no difference on precision when both modeled spectra are used, being observed that the data between first and third quartiles for Barnes et al. [74] are in the range of −4 and 8% and for catalogued spectra [73] between −10 and 1%. These differences between spectra are more evident in Geant4 simulations. All mammographs presented outliers for the evaluation of the relative differences. In an evaluation of all mammographs studied, one may observe (Figure 3d) that the spectrum from [74] was generally more accurate and precise than the spectra from [73]. In the case of Geant4, the simulated absorbed energy seems to present smaller dispersion than the calculated data based on spectra at the detector entrance surface (observe the first and third quartiles in Figure 3d). Even observing this general tendency on data dispersion, it is not possible to conclude that one calculation methodology for the dosimetric quantities is better than the other, since this tendency was only observed for one of the three studied mammographs (Figure 3b).
It is important to note that these are qualitative observations valid for the database (equipment and setups) of this study or similar conditions of energy range and irradiation geometry. To have a quantitative evaluation, one needs to evaluate the statistical significance of the results. Table 2 presents the χ2p value summary to all evaluated cases considering a significance level of 0.05.
The null hypothesis12 is rejected if p value is smaller than the significance level (values highlighted in gray in Table 2). When the null hypothesis is rejected, in this test, one may assume that the compared samples are not from the same population (or are not equal). In Table 2, one may see that, in a general evaluation of HVL, the data collected in Inspiration rejects the null hypothesis for Geant4, evoking liv physics list and spectra from Catalogue [73] for data calculated based on the spectrum that reaches the detector surface. The M3000 is not presenting any null hypothesis rejection. Lorad presents three cases of null hypothesis rejection for HVL values all calculated with Geant4 and the spectra from Catalogue [73]: std physics list considering both calculation methods used (based on spectra and simulated absorbed energy) and pen physics list for simulated absorbed energy. The data for Inspiration and Lorad were collected for different target track-additional filtration combination, so it makes it possible to evaluate the results considering this specific setup characteristic. For Lorad it was possible to observe the null hypothesis rejection for different setups simulated taking into account both target track-additional filtration combination. Comparing the MCCTs, the XRMC presented better agreement to the experimental dataset. In Geant4, the liv physics list presented the lowest, and the std physics list presented the largest number of null hypotheses rejection among the three evaluated Geant4 physics lists. The contingency table with χ2 statistical test was used to evaluate the independence among the possible transport models evoked by each MCCT and the best modeled spectra. A χ2p value of 0.49136 for the comparison among the different transport models (XRMC, Geant4-std, Geant4-pen, Geant4-liv) and a χ2p value of 0.10068 for both modeled spectra were calculated. Both comparisons presented p values above the significance level, showing that not the transport models nor both modeled spectra simulated are not statistically different when normalized data is used (which means comparing the data independently of the total number of photons emitted per mAs for the irradiation area).
Table 3 presents the χ2p value summary comparing the results of XRMC to Geant4 for all evaluated cases considering a significance level of 0.05. Most of the cases evaluated (Table 3) present χ2p values larger than the significance level not rejecting the null hypothesis. It shows that the simulated data for both MCCTs are not statistically different. The exception was Lorad HVL for Geant4 liv Catalogue for absorbed energy calculation due to the track target-additional filtration combination Mo25Rh. This difference did not affect the evaluation considering all cases for each transport model. In a complete evaluation of the simulated data produced by XRMC, the results are statistically compatible (in agreement) to the ones simulated by Geant4 when normalized data are taken into account.
The evaluation same as before was performed with the absolute measurements, first applying the theoretical correction factor, and then the semiempirical correction factor was applied to estimate the number of photons emitted per mAs per total irradiated area. Figure 4 presents the qualitative evaluation for all studied cases and absolute values considering the theoretical correction factor.
Figure 4.
Relative difference between simulated and experimental data considering absolute data, with theoretical correction and showing outliers, for the different modeled spectra and all studied mammographs: Inspiration (a), M3000 (b), Lorad (c), and all equipment (d).
As expected, the relative differences increase when absolute values are compared. This was expected since under this condition the results are dependent of the number of photons emitted per mAs per total irradiated area, considering each setup configuration (peak tension, track target-add filtration combination, and stability of the electrical network associate to the wave rectification of the tube generator). All mammographs presented outlier data, and, in a general observation, one may see that Inspiration setup (Figure 4a) presented again a systematic behavior with median values between 0 and 30% and first and third quartiles between −10 and 80%. In this case, the simulated data overestimated the experimental data. Compared to the results presented in Figure 3a, it suggests that the simulated normalization factor is larger than the experimental one, causing this systematic behavior for normalized HVL to present simulated values that are always smaller than experimental ones. M3000 (Figure 4b) presents few cases with outliers (Geant4 pen transport model and Barnes et al. spectra [74] and XRMC on T mode with Catalogue [73]). As was observed on normalized data (Figure 3b), it presents the best results with median closer to 0% and the first and third quartiles −10 and 35% for all mammographs and different setups evaluated. Lorad (Figure 4c) presents absolute values generally smaller than the experimental data with the median between −14 and 0% and first and third quartiles between −21 and 5% for all evaluated cases. In a general observation of absolute values (Figure 4d), both spectra presented median differences closer to 0%, probably a compensation for the positive systematic tendency presented by Inspiration and the negative systematic tendency presented by Lorad. It shows the importance of evaluating the whole and parts of the database, grouped by characteristics that may influence the simulation, to have better understanding of the curve behaviors and systematic tendencies of the simulated results.
To better evaluate the significance of the findings in Figure 4, it is important to apply a statistical evaluation. Tables 4 and 5 are presenting χ2p values for the validation and the comparison of both MCCTs considering absolute quantities and all mammographs evaluated, applying the theoretical corrections.
Transport models and spectrum identification
Inspiration (HVL Mo30Mo)
Inspiration (HVL Mo25Rh)
M3000 (Mo25Rh)
M3000 (W-50Rh)
XRMC_T–Barnes
<0.001
0.1035
<0.001
<0.001
XRMC_T–Catalogue
<0.001
<0.001
<0.001
0,0453
XRMC_S–Barnes
NA
NA
<0.001
<0.001
XRMC_S–Catalogue
NA
NA
0.0028
0.8740
G4std–Barnes
0.1174
<0.001
<0.001
<0.001
G4std–Barnes–Calc
0.1250
<0.001
<0.001
<0.001
G4std–Catalogue
<0.001
0.9867
<0.001
<0.001
G4std–Catalogue–Calc
<0.001
<0.001
<0.001
<0.001
G4pen–Barnes
0.5026
<0.001
<0.001
<0.001
G4pen–Barnes–Calc
0.7886
<0.001
<0.001
<0.001
G4pen–Catalogue
<0.001
0.9854
<0.001
<0.001
G4pen–Catalogue–Calc
<0.001
<0.001
<0.001
<0.001
G4liv–Barnes
0.1907
<0.001
<0.001
<0.001
G4liv–Barnes–Calc
0.0224
<0.001
<0.001
<0.001
G4liv–Catalogue
<0.001
0.9869
<0.001
<0.001
G4liv–Catalogue–Calc
<0.001
<0.001
<0.001
<0.001
Table 4.
χ2 p values for the validation for both MCCTs considering absolute quantities for all studied cases, applying the theoretical correction factors to define the number of photons emitted per mAs per total irradiated area.
Transport models and spectrum identification
Inspiration (HVL Mo30Mo)
Inspiration (ISL Mo30Mo)
Inspiration (ISL Mo25Rh)
M3000 (Mo30Mo)
Lorad (Mo-XMo)
Lorad (Mo-XRh)
G4std–Barnes
0.9841
0.84732
0.9999
1.000
0.8953
0.05693
G4std–Barnes–Calc
0.0894
0.06821
0.3586
0.5481
0.0249
0.0586
G4std–Catalogue
0.0676
0.0269
0.9685
0.0957
0.6954
0.0568
G4std–Catalogue–Calc
0.05832
0.0384
0.8437
0.7865
0.7864
0.6785
G4pen–Barnes
0.8284
0.0145
0.0725
0.8679
0.0978
0.6604
G4pen–Barnes–Calc
0.6983
0.9421
0.8796
0.5647
0.0413
0.0211
G4pen–Catalogue
0.6753
0.0261
0.2246
0.3540
0.7953
0.7894
G4pen–Catalogue–Calc
0.9485
0.8475
0.1000
0.0039
0.8796
0.6854
G4liv–Barnes
1.0000
0.6735
0.0516
0.7865
0.9999
1.0000
G4liv–Barnes–Calc
0.0768
0.1276
0.6875
0.5694
0.9574
1.0000
G4liv–Catalogue
0.0107
0.0554
0.1534
0.7865
0.7865
0.3451
G4liv–Catalogue–Calc
0.0544
0.0895
0.5674
0.6352
0.4731
0.8966
Table 5.
χ2 p values for the comparison between XRMC and Geant4 (references) considering absolute quantities for all studied cases, applying the theoretical correction factors to define the number of photons emitted per mAs per total irradiated area.
Table 4 is presenting the validation for the mammographs that had at least one p value larger than 0.001. For this reason, the Inspiration (HVL), Inspiration (HVL W50Rh), Inspiration (ISL), Inspiration, (ISL Mo30Mo), Inspiration (ISL Mo25Rh), Inspiration (ISL W50Rh), M3000, M3000 (Mo30Mo), Lorad (Mo-XMo), Lorad (Mo-XRh), and Lorad are not presented.
Table 5 is presenting the χ2p values for the comparison of both MCCTs considering absolute quantities and all options evaluated, applying theoretical correction factor. It only presented the mammographs that had p values larger than 0.001. For this reason, Inspiration (HVL), Inspiration (HVL Mo25Rh), Inspiration (HVL W50Rh, Inspiration (ISL), Inspiration (ISL W50Rh), Inspiration, M3000 (Mo25Rh), M3000 (W50Rh), M3000, and Lorad are not presented.
The χ2 test evaluation presented in Table 5 for absolute values shows a similar result to the ones presented in Table 3 but with a larger number of cases rejecting the null hypothesis and presenting lower p values for each of the studied cases which was expected due to the dependency of the number of photons per mAs for the total area estimated. Only Inspiration ISL Mo25Rh did not present null hypothesis rejection among all evaluated cases. The increase on null hypothesis rejection, comparing XRMC to Geant4, is related to the small statistical fluctuation presented by the MCCTs (between 0.2 and 1.5%) when compared to experimental data.
Based on the p values presented in Table 4, one could conclude that both MCCTs are not valid for this kind of simulation. However, the p values presented for normalized data (Tables 2 and 3) show that the tendencies of the normalized quantities for the simulated data using both MCCTs can be considered statistically non-different to the experimental data. Besides that, the absolute data comparison between both MCCTs (Table 4) presented no null hypothesis rejection. In this case, it is important to verify if the total number of photons defined by the theoretical correction factor applied to the spectra produced a systematic tendency on the expected curves. It is important as well to note that the evaluation is consistent when the normalized data shows no significant difference in the validation process. The curves used in this study to estimate the semiempirical correction factor were:
HVL—the curve of KERMA as function of the additional Al filtration thickness for the same acceleration voltage
ISL—the tendency of the KERMA as function of the distance between focal spot and detector surface for the same acceleration voltage
BS—the tendency of the KERMA as function of the thickness of the scatterer considering the scatterer (or considering the backscattered radiation) and the tendency of the KERMA as function of the thickness of the scatterer without considering the scatterer (or not considering the backscattered radiation)
All cases used to generate the semiempirical correction factor considered the best GoF test results for the amplitude when applied to the simulated data for one acceleration voltage and track target-additional filtration combination for a specific mammograph. The best value for the amplitude in each case was used as semiempirical correction factor to be applied as a multiplication factor on the theoretical correction factor for the total number of photons per mAs per total irradiated area.
Tables 6 and 7 are presenting the χ2p values for the validation of both MCCTs considering absolute quantities and all cases evaluated, applying the semiempirical correction factors to define the number of photons emitted per mAs per total irradiated area.
Transport models and spectrum identification
Inspiration (HVL)
Inspiration (ISL)
M3000 (BS)
Lorad (HVL)
All
M3000 (Mo30Mo)
M3000 (Mo25Rh)
M3000 (W-25Rh)
Lorad (Mo30Mo)
Lorad (Mo25Rh)
XRMC_T–Barnes
0.2502
0.0754
NA
1.0000
0.9889
NA
NA
NA
1.0000
0.7265
XRMC_T–Catalogue
0.0603
0.0564
NA
0.5859
0.2123
NA
NA
NA
0.82734
0.1466
XRMC_S–Barnes
NA
NA
1.0000
NA
1.0000
1.0000
0.8009
1.0000
NA
NA
XRMC_S–Catalogue
NA
NA
1.0000
NA
1.0000
0.9990
0.9990
1.0000
NA
NA
G4std–Barnes
0.2635
0.2384
0.9987
0.0817
0.7669
0.9871
0.9987
1.0000
0.8996
<0.001
G4std–Barnes–Calc
0.1006
0.0845
1.0000
0.0785
0.8876
0.8997
0.9946
1.0000
0.1073
0.2069
G4std–Catalogue
0.1182
<0.001
1.0000
<0.001
0.3636
0.9999
0.8954
1.0000
<0.001
<0.001
G4std–Catalogue–Calc
0.0653
<0.001
1.0000
<0.001
0.0457
1.0000
0.9997
1.0000
0.0819
0.0211
G4pen–Barnes
0.1398
0.1294
0.9998
0.1101
1.0000
1.0000
1.0000
1.0000
0.9989
0.0521
G4pen–Barnes–Calc
0.1263
0.5643
1.0000
0.7587
1.0000
1.0000
0.9982
1.0000
0.9897
0.0597
G4pen–Catalogue
0.2151
<0.001
0.9988
<0.001
0.0381
0.9675
0.9999
1.0000
0.0467
<0.001
G4pen–Catalogue–Calc
0.2299
<0.001
0.8999
0.0302
0.0569
0.9999
1.0000
1.0000
0.3747
0.0138
G4liv–Barnes
0.1946
0.1112
0.9979
0.9703
1.0000
1.0000
1.0000
1.0000
1.0000
0.1435
G4liv–Barnes–Calc
0.2384
0.1349
0.9977
0.0690
1.0000
1.0000
0.9734
1.0000
0.0528
0.2694
G4liv–Catalogue
0.7910
<0.001
0.0357
0.0490
0.0428
0.9863
1.0000
1.0000
0.0521
0.7092
G4liv–Catalogue–Calc
0.0301
<0.001
0.8073
0.5762
0.0665
1.0000
0.9763
1.0000
0.1454
0.8968
Table 6.
χ2 p values for the validation for both MCCTs considering absolute quantities for all studied cases, applying the semiempirical correction factors to define the number of photons emitted per mAs per total irradiated area.
Transport models and spectrum identification
Inspiration (HVL)
Inspiration (ISL)
M3000 (BS)
Lorad (HVL)
All
M3000 (Mo30Mo)
M3000 (Mo25Rh)
M3000 (W-25Rh)
Lorad (Mo30Mo)
Lorad (Mo25Rh)
G4std–Barnes
0.9777
0.2463
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
0.9999
G4std–Barnes–Calc
0.9149
0.1966
1.0000
0.9671
1.0000
1.0000
1.0000
1.0000
0.6334
0.9972
G4std–Catalogue
0.2139
0.1481
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
0.9808
1.0000
G4std–Catalogue–Calc
0.1595
0.0710
1.0000
0.9975
1.0000
1.0000
1.0000
1.0000
0.9200
0.9974
G4pen–Barnes
0.8606
0.2494
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
0.9999
G4pen–Barnes–Calc
0.7994
0.3756
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
0.9637
G4pen–Catalogue
0.1660
0.1910
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
G4pen–Catalogue–Calc
0.1572
0.0331
1.0000
0.0002
0.4832
1.0000
1.0000
1.0000
1.0000
0.0401
G4liv–Barnes
0.9767
0.1595
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
0.9997
G4liv–Barnes–Calc
0.6809
0.8606
1.0000
0.8765
1.0000
1.0000
1.0000
1.0000
0.4828
1.0000
G4liv–Catalogue
0.7014
0.7994
1.0000
0.9212
0.9994
1.0000
1.0000
1.0000
1.0000
1.0000
G4liv–Catalogue–Calc
0.6993
0.1660
1.0000
0.1663
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
Table 7.
χ2 p values for the comparison between XRMC and Geant4 (references) considering absolute quantities for all studied cases, applying the semiempirical correction factors to define the number of photons emitted per mAs per total irradiated area.
The application of semiempirical correction factors shows a better approximation for absolute values. When one compares the results corrected by the theoretical factors (Table 4) to the results corrected by theoretical factors associated to semiempirical factors (Table 6), the increase of cases that did not reject the null hypothesis is visible. With the exception of Geant4 std (Barnes et al. [74]), all the other cases that rejected the null hypothesis are all from Catalogue [73] which shows that for absolute values and the semiempirical methodology used to generate the correction factor; spectrum of Barnes et al. [74] was the one that presented better agreement to experimental data. In the overall evaluation for each studied case comparing each MCCT and transport model, three cases simulated using Catalogue [73] spectra presented χ2p values below the significance level: Genat4 std and liv for Calculated absorbed energy and Geant4 pen. All the other χ2p values are above the significance level. To conclude, the validation of absolute values for all studied cases (column “All” on Table 6), when semiempirical correction factors are applied for both MCCTs, Geant4 MCCT seems to present more sensitivity to the changes in the spectra showing significant differences (not agree) from experimental data for three simulated cases using spectra from Catalogue [73]. This can be due to the more detailed transport of primary and secondary particles. Considering Barnes et al.’s [74] spectra, there is no significant difference between experimental and simulated data considering the results for both MCCTs.
The comparison between both MCCTs after applying the semiempirical correction factor is presented in Table 7. As was expected there was an increase of the p values for the absolute value comparison of both MCCTs (Table 7) when compared to the validation of both MCCTs (Table 6). This is expected since the relative differences presented between simulated results (XRMC compared to Geant4) are smaller than the presented between each MCCT and experimental data. It is also important to note that for the comparison between both MCCTs only differences among the transport models evoked are significant. However, on a validation there may be differences associated to minimal discrepancies between experimental and simulated geometry, discrepancies among the transport models evoked (limitations of each model) and the repeatability of the X-radiation production and technical parameters of the mammograph. In the example presented in this section, the introduction of the modeled primary beam increases one variable to be considered in this context, increasing the error associated to the estimation of total number of proton emitted per mAs per total irradiated area. However, when one uses a code or model available on the X-ray equipment to estimate the dose in a radiological procedure, this person is using a modeled spectra or an estimated average spectra for the equipment and needs to pay attention to the limitations of this methodological choice.
To compare the results generated by both MCCTs directly, the χ2 Pearson, Anderson-Darling, and Kolmogorov-Smirnov tests were applied on the simulated spectra at the entrance surface of the sensitive volume. These spectra were compared, and all of the studied cases presented p values above the significance level. For χ2 Pearson test, all p values were 1.0000. The cases that presented larger differences on the validation, such as absolute values for M3000 XRMC and Geant4 based on Catalogue [73] (Tables 8 and 9), presented the lower p values in all statistical tests performed for the comparison of the MCCT.
Transport models and spectrum identification
Inspiration
M3000
Lorad
All
G4std–Barnes
0.9149
0.6566
0.9671
1.0000
G4std–Catalogue
0.1595
0.0521
0.9975
1.0000
G4pen–Barnes
0.7994
0.1182
1.0000
1.0000
G4pen–Catalogue
0.1572
0.0653
0.9975
0.4832
G4liv–Barnes
0.6809
0.1398
0.8765
1.0000
G4liv–Catalogue
0.6993
0.1263
0.1663
1.0000
Table 8.
Anderson-Darling p values for the comparison between XRMC and Geant4 (references) considering the spectrum at detector entrance surface for all physics lists and studied cases.
Transport models and spectrum identification
Inspiration
M3000
Lorad
All
G4std–Barnes
1.0000
0.8671
1.0000
0.9768
G4std–Catalogue
0.9999
0.9975
1.0000
0.9999
G4pen–Barnes
1.0000
1.0000
1.0000
1.000
G4pen–Catalogue
1.0000
1.0000
0.9999
1.0000
G4liv–Barnes
0.9998
0.8765
1.0000
0.9154
G4liv–Catalogue
1.0000
0.1663
1.0000
0.3687
Table 9.
Kolmogorov-Smirnov p values for the comparison between XRMC and Geant4 (references) considering the spectrum at detector entrance surface for all physics lists and studied cases.
Another important characteristic of MCCT to take into account is the running time. In this example, the XRMC Transmission mode reduced the running time around 2.5 times compared to Geant4 std physics list, 4 times compared to Geant4 pen physics list and 4.5 compared to Geant4 liv physics list. However, the limitations on simulating the absorbed energy and statistic fluctuations for this XRMC version make the data treatment slower than that used on Geant4 and dependent of several external tools to perform data analyses that are not needed in Geant4.
When the experimental spectra of the X-ray equipment (in this example for mammographs) are available, it is better to use the experimental ones and the correction factors associated to it. However, it is important to keep in mind that it should be the spectra generated by the X-ray tube that is being used, since each tube (even the ones with the same characteristics produced by the same manufacturer) may have a difference on efficiency conversion due to minimal differences in its manufacturing. Besides that, a periodical verification of the amplitude correction factor for the number of photons generated per mAs per total irradiated area (or solid angle) must be applied since the tube wear can affect the conversion efficiency due to the deposition of atoms of the track-target on the window surface (by sputtering effect) or by the releasing of atoms from the track-target into the volume of the tube low pressure air.
4. Final considerations
The objective of this chapter was to present the main concepts of validation and reliability applied to MC application development to dosimetry and imaging, presenting a minimal validation that can be performed by MCCT ADs. It is important to note, as an AD in MC, that it is always valid to have your own experimental data to validate the application in the contour limitations of your problem. If experimental data for validation or modeled data for comparison are not available; at least a reliability test should be performed to ensure the quality of the results generated by the MCCT.
On choosing a MCCT, one needs to pay attention to the characteristics of the application, the capabilities and limitations of the MCCT code, and its computational performance. Besides that, the best MCCT is the one that the AD knows how to use (installing, developing applications, and extracting useful data). To do that the AD needs to have knowledge of a programing language or, at least, to understand the logic of input data in MCCT, to understand the experiment or clinical reality to be described in the simulation, and to have the notions of the processes and models of transport significant to the study case.
Regarding the results for the example used in this chapter the evaluation presented as follows:
Validation—the statistical evaluation presented no null hypothesis rejection for XRMC results and presented the rejection of null hypothesis for few Geant4 cases evaluated considering normalized data. The XRMC presented the best agreement to the experimental data. Considering Geant4 the Livermore was the best physic list option. For absolute quantities calculated by applying semiempirical correction factors, all mammographs presented χ2p value under the significance level: one value for Inspiration (HVL) and one M3000 (BS) and few for Lorad (Mo25Rh and Mo30Mo) and Inspiration (ISL). Despite these particular cases of null hypothesis rejection, the overall evaluation for each transport model considering all studied cases presented few null hypothesis rejections for Geant4 MCCT using Catalogue spectra. So, it is recommendable to use spectra from Barnes et al. that were validated using both MCCTs (XRMC and Geant4). The use of only the theoretical correction factor for absolute quantities is not encouraged to perform validation, unless the AD knows pretty well the total number of photons emitted by the tube for the irradiation condition. Normalized data may be used associated to theoretical spectra to understand behaviors and tendencies of dosimetric quantities and to explore the influence of changes in the data acquisition but not to define absolute quantities.
Comparison—the spectra generated at the entrance surface of the detector by both MCCTs always presented p values above the significance level of 0.05 for normalized data, showing that for this case the spectra generated by the same setup were from the same population (equal) within statistical significance. For absolute quantities calculated by applying semiempirical correction factors, one p value was under the significance level for Lorad (Mo25Rh) and one for Inspiration (ISL). Despite of these particular cases of null hypothesis rejection, the overall evaluation for each transport model considering all the evaluated cases presented no significant difference between XRMC and Geant4 which is compatible with the internal consistency of the transport models evoked.
Reliability—the qualitative reliability evaluation based on graphics makes possible to observe that the more consistent data occurs for the simulation of the M3000. The graphics allowed to observe the tendencies when comparing simulated data to experimental data considering overall data and specific subgroups. This visual observation shows a consistency with the statistical tables, presenting sensitivity to help on data classification for a detailed analysis.
The methods to test a MCCT application are indispensable in the good practice of computational dosimetry and imaging because they guarantee the quality of the results, helping on the evaluation of the methodology limitations and making it possible to improve the trustability of the application and its results transposing with safety the “computational world” to the “real world.”
\n',keywords:"Monte Carlo, mammography, medical physics, XRMC, Geant4",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/68931.pdf",chapterXML:"https://mts.intechopen.com/source/xml/68931.xml",downloadPdfUrl:"/chapter/pdf-download/68931",previewPdfUrl:"/chapter/pdf-preview/68931",totalDownloads:256,totalViews:0,totalCrossrefCites:0,dateSubmitted:"March 27th 2019",dateReviewed:"July 29th 2019",datePrePublished:"September 5th 2019",datePublished:"December 18th 2019",dateFinished:null,readingETA:"0",abstract:"In this chapter, the Monte Carlo (MC) core is presented, particularly its cross-sectional libraries and random generators. The main idea is to introduce validation and reliability of MC applications and to explore its limitations. As an example, a comparison between two MC toolkits, namely XRMC (version 6.5.0–2) and Geant4 (version 10.02.p02), and a validation between each of them and experimental data applied to mammography (external dosimetry) are presented. The simulated quantities compared are exposure, kerma, half-value layer, and backscattering. Limitations, advantages, and disadvantages of using a general and specific MC toolkit are commented too.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/68931",risUrl:"/chapter/ris/68931",signatures:"Gabriela Hoff, Bruno Golosio, Elaine E. Streck and Viviana Fanti",book:{id:"8190",title:"Theory, Application, and Implementation of Monte Carlo Method in Science and Technology",subtitle:null,fullTitle:"Theory, Application, and Implementation of Monte Carlo Method in Science and Technology",slug:"theory-application-and-implementation-of-monte-carlo-method-in-science-and-technology",publishedDate:"December 18th 2019",bookSignature:"Pooneh Saidi Bidokhti",coverURL:"https://cdn.intechopen.com/books/images_new/8190.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"22336",title:"Dr.",name:"Pooneh",middleName:null,surname:"Saidi",slug:"pooneh-saidi",fullName:"Pooneh Saidi"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"299864",title:"Ph.D.",name:"Gabriela",middleName:null,surname:"Hoff",fullName:"Gabriela Hoff",slug:"gabriela-hoff",email:"ghoff.gesic@gmail.com",position:null,institution:null},{id:"310011",title:"Prof.",name:"Bruno",middleName:null,surname:"Golosio",fullName:"Bruno Golosio",slug:"bruno-golosio",email:"golosio@unica.it",position:null,institution:{name:"University of Cagliari",institutionURL:null,country:{name:"Italy"}}},{id:"310012",title:"Dr.",name:"Elaine",middleName:null,surname:"Streck",fullName:"Elaine Streck",slug:"elaine-streck",email:"elainestreck@gmail.com",position:null,institution:null},{id:"310013",title:"Prof.",name:"Viviana",middleName:null,surname:"Fanti",fullName:"Viviana Fanti",slug:"viviana-fanti",email:"viviana.fanti@ca.infn.it",position:null,institution:{name:"University of Cagliari",institutionURL:null,country:{name:"Italy"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Monte Carlo general concepts and core",level:"1"},{id:"sec_2_2",title:"2.1 General versus specific Monte Carlo toolkit for radiation transport",level:"2"},{id:"sec_4",title:"3. Verification, validation, comparison, and reliability of Monte Carlo toolkits",level:"1"},{id:"sec_4_2",title:"3.1 Example of application for macroscopic validation, comparison, and reliability for XRMC and Geant4",level:"2"},{id:"sec_6",title:"4. Final considerations",level:"1"}],chapterReferences:[{id:"B1",body:'Leclerc GL. Des Sciences: Géométrie. In: De L\' Imprimerie Royale (France). Histoire de l\'Académie Royale des Sciences. 1735. pp. 43–45. Available from: https://play.google.com/books/reader?id=GOAEAAAAQAAJ'},{id:"B2",body:'Leclerc GL. Essai D’Arithmétique Morale. In: De L\' Imprimerie Royale (France). Histoire naturelle, générale et particulière, servant de suite à la Théorie de la Terre et d\'introduction à l\'Histoire des Minéraux. 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May/Jun 1991;179:143–148, April 1991'}],footnotes:[{id:"fn1",explanation:"RUN: word used to define the execution of the MC code."},{id:"fn2",explanation:"EVENT: every interaction that happened to one primary particle or its secondaries until they die or leave the universe of simulation. It is defined as the collections of steps performed by one particle."},{id:"fn3",explanation:"All attenuation cross sections used were from XCOM NIST (https://physics. nist.gov/cgi-bin/Xcom/xcom3_2)."},{id:"fn4",explanation:"Microscopic validation: refers to the detailed validation of microscopic quantities (usually the libraries) used by the MC code to generate the quantitative results. See more information on Section 3. Verification, validation, comparison, and reliability of Monte Carlo toolkit."},{id:"fn5",explanation:"The Geant4 list of particles and its identifications number may be found at https://www.star.bnl.gov/public/comp/simu/newsite/gstar/Manual/particle_id.html)."},{id:"fn6",explanation:"The FLUKA list of particles and its identifications number may be found at http://www.fluka.org/content/manuals/online/5.1.html."},{id:"fn7",explanation:"EPDL: Photon and Electron Interaction Data is available at https://www-nds.iaea.org/epdl97."},{id:"fn8",explanation:"XCOM: Photon Cross-sectional Database is available at https://www.nist.gov/pml/xcom-photon-cross-sections-database."},{id:"fn9",explanation:"Additional information about SUGMA function access SectionB.2 in Appendix B of the PENELOPE-2014: A Code System for Monte Carlo Simulation of Electron and Photon Transport at https://www.oecd-nea.org/science/docs/2015/nsc-doc2015-3.pdf"},{id:"fn10",explanation:"The EGSnrc has its official page associate to National Research Council Canada at https://nrc.canada.ca/en/research-development/products-services/software-applications/egsnrc-software-tool-model-radiation-transport."},{id:"fn11",explanation:"Search for the active standards was performed at https://standards.ieee.org; https://www.en-standard.eu and https://www.iso.org/about-us.html."},{id:"fn12",explanation:"χ2 test null hypothesis: relationship between experimental and simulated data does not exist, which means these samples are presenting the same distribution."}],contributors:[{corresp:"yes",contributorFullName:"Gabriela Hoff",address:"ghoff.gesic@gmail.com",affiliation:'
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He is\na current member of the Neuromechanics of Human Movement research group\nwith a special interest in neurorehabilitation and in the role of physical activity on\nregeneration of the peripheral nerves. He collaborates with other groups working\nin peripheral nerve research and regenerative medicine, which share an interest\nin developing novel solutions for the reconstruction of damaged nerves and how\nthey might be combined with rehabilitation, in order to develop a comprehensive\napproach for the treatment of peripheral nerve damage.",institutionString:null,institution:null},{id:"161689",title:"Dr.",name:"Cátia",surname:"Pereira",slug:"catia-pereira",fullName:"Cátia Pereira",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"169686",title:"Dr.",name:"Haigang",surname:"Gu",slug:"haigang-gu",fullName:"Haigang Gu",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"169687",title:"Dr.",name:"Rahul",surname:"Nath",slug:"rahul-nath",fullName:"Rahul Nath",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"169688",title:"Dr.",name:"Takashi",surname:"Kawano",slug:"takashi-kawano",fullName:"Takashi Kawano",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Kochi Medical School Hospital",institutionURL:null,country:{name:"Japan"}}},{id:"169925",title:"Dr.",name:"Qi",surname:"Zhang",slug:"qi-zhang",fullName:"Qi Zhang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"169926",title:"Dr.",name:"Chandra",surname:"Somasundaram",slug:"chandra-somasundaram",fullName:"Chandra Somasundaram",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"169928",title:"Dr.",name:"Ana",surname:"Luis",slug:"ana-luis",fullName:"Ana Luis",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null}]},generic:{page:{slug:"women-in-science-program",title:"IntechOpen Women in Science Program",intro:"
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At IntechOpen, we’re laying the foundations for the future by publishing the best research by women in STEM – Open Access and available to all. Our Women in Science program already includes six books in progress by award-winning women scientists on topics ranging from physics to robotics, medicine to environmental science. Our editors come from all over the globe and include L’Oreal–UNESCO For Women in Science award-winners and National Science Foundation and European Commission grant recipients.
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“My scientific path has given me the opportunity to work with colleagues all over Europe, including Germany, France, and Norway. Editing the book Graph Theory: Advanced Algorithms and Applications with IntechOpen emphasized for me the importance of providing valuable, Open Access literature to our scientific colleagues around the world. So I am highly enthusiastic about the Women in Science book collection, which will highlight the outstanding accomplishments of women scientists and encourage others to walk the challenging path to becoming a recognized scientist." Beril Sirmacek, TU Delft, The Netherlands
At IntechOpen, we’re laying the foundations for the future by publishing the best research by women in STEM – Open Access and available to all. Our Women in Science program already includes six books in progress by award-winning women scientists on topics ranging from physics to robotics, medicine to environmental science. Our editors come from all over the globe and include L’Oreal–UNESCO For Women in Science award-winners and National Science Foundation and European Commission grant recipients.
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We aim to publish 100 books in our Women in Science program over the next three years. We are looking for books written, edited, or co-edited by women. Contributing chapters by men are welcome. As always, the quality of the research we publish is paramount.
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All project proposals go through a two-stage peer review process and are selected based on the following criteria:
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scientific excellence,
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uptake of recent trends in the corresponding field,
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originality,
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monthly public lectures, held in our offices in London and livestreamed;
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bi-weekly Coffee with the Author podcasts, where one of our scientists gives an insight into her work;
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devoted social media channels;
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advertising at relevant conferences and industry events.
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Interested? If you have an idea for an edited volume or a monograph, we’d love to hear from you! Contact Ana Pantar at book.idea@intechopen.com.
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“My scientific path has given me the opportunity to work with colleagues all over Europe, including Germany, France, and Norway. Editing the book Graph Theory: Advanced Algorithms and Applications with IntechOpen emphasized for me the importance of providing valuable, Open Access literature to our scientific colleagues around the world. So I am highly enthusiastic about the Women in Science book collection, which will highlight the outstanding accomplishments of women scientists and encourage others to walk the challenging path to becoming a recognized scientist." Beril Sirmacek, TU Delft, The Netherlands
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