<scp>famoz</scp>: a software for parentage analysis using dominant, codominant and uniparentally inherited markers

Gerber, Sophie; Chabrier, Patrick; Kremer, Antoine

doi:10.1046/j.1471-8286.2003.00439.x

Cited by 205 publications

(214 citation statements)

References 6 publications

(10 reference statements)

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“…The resulting DNA fingerprints were used to match juveniles with potential parents in a log-likelihood statistical framework 31 . We assigned 407 (28.1% of those sampled) juvenile A. percula independently to both parents in the same anemone in 2009, and similarly 437 (28.2%) juveniles were assigned in 2011.…”

Section: Resultsmentioning

confidence: 99%

Larval fish dispersal in a coral-reef seascape

et al. 2017

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Larval dispersal is a critical yet enigmatic process in the persistence and productivity of marine metapopulations. Empirical data on larval dispersal remain scarce, hindering the use of spatial management tools in efforts to sustain ocean biodiversity and fisheries. Here we document dispersal among subpopulations of clownfish (Amphiprion percula) and butterflyfish (Chaetodon vagabundus) from eight sites across a large seascape (10,000 km 2 ) in Papua New Guinea across 2 years. Dispersal of clownfish was consistent between years, with mean observed dispersal distances of 15 km and 10 km in 2009 and 2011, respectively. A Laplacian statistical distribution (the dispersal kernel) predicted a mean dispersal distance of 13-19 km, with 90% of settlement occurring within 31-43 km. Mean dispersal distances were considerably greater (43-64 km) for butterflyfish, with kernels declining only gradually from spawning locations. We demonstrate that dispersal can be measured on spatial scales sufficient to inform the design of and test the performance of marine reserve networks. R obust descriptions of larval dispersal are fundamental to studies of fish population dynamics 1,2 , fisheries management 3,4 and the design of reserve networks tasked with conserving ocean biodiversity 5,6 . Yet descriptions of larval dispersal patterns in ocean environments remain scarce. The combination of a pelagic larval phase that may last several days to many months and an ocean environment characterized by energetic diffusive and advective flows may allow passive larvae to disperse hundreds to thousands of kilometres from natal locations 7,8 . It has proved difficult, however, to verify directly how far fish larvae travel, because it is almost impossible to follow them as they disperse rapidly from spawning sites and are subject to high rates of natural mortality throughout the larval phase 9 . Our inability to describe the spatial extent of larval dispersal is problematic because our understanding of metapopulation dynamics relies on largely untested models that quantify where larvae arriving at a subpopulation originate from and where larvae spawned at each subpopulation eventually settle [10][11][12] . Moreover, to be of practical use, these data must be assembled on large enough scales for evaluating and optimizing spatial management strategies for fisheries or conservation 1,13 .Patches of reef habitat are frequently isolated from each other by deeper water that forms a barrier to adult movement, and so larval dispersal is likely to be a critical process in the persistence of many reef fish populations over demographic and evolutionary timescales 10,14 . Effective management of coral-reef seascapes is therefore particularly reliant on spatial tools to achieve conservation objectives. Although reef fish larvae clearly have the potential for long-distance movements 14 , there is increasing evidence that dispersal may be more limited than previously assumed 15,16 . The most compelling evidence of larvae returning to natal or nearby reefs has...

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Section: Resultsmentioning

confidence: 99%

Larval fish dispersal in a coral-reef seascape

et al. 2017

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“…For SpAC03, EATC1B02, EATC2B02 and EATC1E03 primers reaction mix consists of 10 ng of template DNA, 100 mM of each dNTP, 10 Â Fermentas PCR buffer (100 mM Tris-HCl (pH 8.8 at 25 1C), 500 mM KCl, 0.8% Nonidet P40), 2.5 mM MgCl 2 , 0.1 mM forward primer and 0.25 mM reverse primer, 1 U Taq Polymerase (Fermentas, Glen Burnie, MD, USA). For SpAGG03 primer reaction mix consists of 10 ng of template DNA, 100 mM of each dNTP, 10 Â Fermentas PCR buffer (100 mM Tris-HCl (pH 8.8 at 25 1C), 500 mM KCl, 0.8% Nonidet P40), 2.5 mM Table 1 Data analysis Parentage analysis was carried out using the software FaMoz (Gerber et al, 2003). First, the variability and the resolution of our marker set were evaluated.…”

Section: Dna Extraction and Microsatellite Analysismentioning

confidence: 99%

Spruce colonization at treeline: where do those seeds come from?

et al. 2009

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At treeline, selection by harsh environmental conditions sets an upward limit to arboreal vegetation. Increasing temperatures and the decline of traditional animal raising have favoured an upward shift of treeline in the last decades. These circumstances create a unique opportunity to study the balance of the main forces (selection and gene flow) that drive tree migration. We conducted a parentage analysis sampling and genotyping with five microsatellite markers in all Norway spruce individuals (342 juveniles and 23 adults) found in a recently colonized treeline area (Paneveggio forest, Eastern Alps, Italy). Our goal was to evaluate local reproductive success versus gene flow from the outside. We were able to identify both parents among local adults for only 11.1% of the juveniles. In the gamete pool we sampled, two-thirds were not produced locally. Effective seed dispersal distance distribution was characterized by a peak far from the seed source (mean 344.66 m ± 191.02 s.d.). Reproductive success was skewed, with six local adults that generated almost two-thirds (62.4%) of juveniles with local parents. Our findings indicate that, although a few local adults seem to play an important role in the colonization process at treeline, large levels of gene flow from outside were maintained, suggesting that the potential advantages of local adults (such as local adaptation, proximity to the colonization area, phenological synchrony) did not prevent a large gamete immigration.

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“…The program has been used for paternity analysis of Pinus pinaster at ITQB-IBET (www.itqb.unl.pt), and its results fully agree with other software namely FaMoz ( [7]), Cervus 2.0 ( [6]) and MLTR 2. 2 ([8]).…”

Section: Computational Methods and Theorymentioning

confidence: 61%

“…There are different public domain programs, like FaMoz ( [7]), Cervus 2.0 ( [6]) and MLTR 2.2 ( [8]), that do some of the computations required for paternity analysis but they are not necessarily user-friendly. For a sporadic user it takes a lot of time and effort to understand how the input and the output works.…”

Section: Introductionmentioning

confidence: 99%

Paternity analysis in Excel

Rocheta

Dionísio²,

Fonseca³

et al. 2007

Computer Methods and Programs in Biomedicine

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Paternity analysis using microsatellite information is a well studied subject. These markers are ideal for parentage studies and fingerprinting, due to their high discrimination power. This type of data is used to assign paternity, to compute the average selfing and outcrossing rates and to estimate the biparental inbreeding. There are several public domain programs that compute all this information from data. Most of the time, it is necessary to export data to some sort of format, feed it to the program and import the output to an Excel book for further processing. In this article we briefly describe a program referred from now on as PAE (Paternity Analysis in Excel), developed at IST and IBET (see the acknowledgments) that computes paternity candidates from data, and other information, from within Excel. In practice this means that the end user provides the data in an Excel sheet and, by pressing an appropriate button, obtains the results in another Excel sheet. For convenience PAE is divided into two modules. The first one is a filtering module that selects data from the sequencer and reorganizes it in a format appropriate to process paternity analysis, assuming certain conventions for the names of parents and offspring from the sequencer. The second module carries out the paternity analysis assuming that one parent is known. Both modules are written in Excel-VBA and can be obtained at the address www.math.ist.utl.pt/∼fmd/pa/pa.zip. They are free for non-commercial purposes and have been tested with different data and against different software (Cervus, FaMoz, and MLTR).

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famoz: a software for parentage analysis using dominant, codominant and uniparentally inherited markers

Cited by 205 publications

References 6 publications

Larval fish dispersal in a coral-reef seascape

Larval fish dispersal in a coral-reef seascape

Spruce colonization at treeline: where do those seeds come from?

Paternity analysis in Excel

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