Detecting genotyping errors and describing American black bear movement in northern Idaho

Schwartz, Michael K.; Cushman, Samuel A.; McKelvey, Kevin S.; Hayden, Jim; Engkjer, Cory

doi:10.2192/1537-6176(2006)17[138:dgeada]2.0.co;2

Cited by 48 publications

(44 citation statements)

References 45 publications

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“…All individuals were given 2 alleles per locus for 15 independent diploid, co-dominant loci, each with 7 alleles per locus. We assigned alleles to each locus by randomly drawing individual alleles numbered between 1 and 7 in the following proportions 1 = 0.38, 2 = 0.24, 3 = 0.17, 4 = 0.08, 5 = 0.05, 6 = 0.04, and 7 = 0.03, similar to microsatellite allele distributions associated with several wildlife species that we currently study (e.g., Schwartz et al 2003Schwartz et al , 2004Schwartz et al , 2006. The initial population showed no spatial autocorrelation.…”

Section: Methodsmentioning

confidence: 99%

“…However, with corner sampling only a subset of individuals are captured within each sampling area. This type of sampling is common with ungulates, marine fish, and carnivores (e.g., Pardini et al 2001;Schwartz et al 2006;Hicks et al 2007). (D) Line transect sampling-10 equally spaced lines of 36 individuals were sampled following the study design of many small mammal, forest carnivore, and plant studies (e.g., Gamache et al 2003).…”

Section: Methodsmentioning

confidence: 99%

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Why sampling scheme matters: the effect of sampling scheme on landscape genetic results

2008

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There has been a recent trend in genetic studies of wild populations where researchers have changed their sampling schemes from sampling pre-defined populations to sampling individuals uniformly across landscapes. This reflects the fact that many species under study are continuously distributed rather than clumped into obvious ''populations''. Once individual samples are collected, many landscape genetic studies use clustering algorithms and multilocus genetic data to group samples into subpopulations. After clusters are derived, landscape features that may be acting as barriers are examined and described. In theory, if populations were evenly sampled, this course of action should reliably identify population structure. However, genetic gradients and irregularly collected samples may impact the composition and location of clusters. We built genetic models where individual genotypes were either randomly distributed across a landscape or contained gradients created by neighbor mating for multiple generations. We investigated the influence of six different sampling protocols on population clustering using program STRUCTURE, the most commonly used model-based clustering method for multilocus genotype data. For models where individuals (and their alleles) were randomly distributed across a landscape, STRUCTURE correctly predicted that only one population was being sampled. However, when gradients created by neighbor mating existed, STRUCTURE detected multiple, but different numbers of clusters, depending on sampling protocols. We recommend testing for fine scale autocorrelation patterns prior to sample clustering, as the scale of the autocorrelation appears to influence the results. Further, we recommend that researchers pay attention to the impacts that sampling may have on subsequent population and landscape genetic results.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Why sampling scheme matters: the effect of sampling scheme on landscape genetic results

2008

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“…To avoid erroneous results arising out of genotyping error caused by nonreplicable results or the nonamplification of an allele in heterozygotes ('allelic dropout'), all results were subject to the appropriate amount of replication given the quantity of DNA present as detailed by Arandjelovic et al [22]. However, such a 'multitube' approach cannot detect errors that may be created when scoring or transcribing data into a database [25]. We therefore also used two screening methods, the examining bimodality and difference in capture history tests, implemented in the DROPOUT package to detect genotyping errors [26].…”

Section: Error Controlmentioning

confidence: 99%

Noninvasive genetic assessment of the population trend and sex ratio of the Shennongjia population of Sichuan snub-nosed monkeys (Rhinopithecus roxellana)

Chang

Liu

Yang³

et al. 2011

Chin. Sci. Bull.

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Effective population management relies on assessments of population size and sex ratio. However, these estimates are difficult to obtain for elusive and rare species. Recently, noninvasive genetic census methods have been developed as an alternative to traditional capture-mark-recapture methods. In this study, we estimated the size of the Sichuan snub-nosed monkey (Rhinopithecus roxellana) population in the Shennongjia Nature Reserve (SNR) using a noninvasive sampling method based on 16 microsatellite loci. We also used a PCR-based genetic method to sex the sampled individuals and infer the population sex ratio. The population size of R. roxellana in the SNR was estimated to be 1044 individuals (95% CI TIRM : 613-1409). The estimated population sex ratio is more female-biased than expected, which we attribute to the sampling biased towards one male units and limited sampling of bachelor male units. Moreover, there is no suggestion that the heavy traffic road through the reserve might block movement of monkeys. The results of this study indicate genetic assessments based on a noninvasive sampling method can provide useful information regarding populations of elusive primates.

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“…However, microsatellites' high mutation rates and variations in amplification stability of primers can lead to genotyping errors due to the low quantities and/or quality of DNA in feces and hair (Taberlet et al, 1996;Waits and Leberg, 2000;Roon et al, 2005;Schwartz et al, 2006). In order to guarantee data efficiency, we used MICRO-CHECHER software (Van Oosterhout et al, 2004) to estimate the presence of genotyping errors such as null alleles, large allele dropout or stuttering null alleles in the data set.…”

Section: Discussionmentioning

confidence: 99%

Microsatellite variability reveals significant genetic differentiation of giant pandas (Ailuropoda melanoleuca) in the Minshan A habitat

Yang¹,

Hou²,

Shen³

et al. 2011

Afr. J. Biotechnol.

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Although, Minshan A habitat is an area with one of the largest numbers of wild giant pandas (Ailuropoda melanoleuca); it may be threatened by habitat loss and fragmentation. In this study, 10 microsatellite DNA markers were used to assess population genetic structure of giant pandas from two critical reserves (Tangjiahe and Wanglang) in the Minshan A habitat. The results revealed high levels of genetic differentiation (F ST = 0.134) between the two populations. This differentiation was supported by the assignment tests using the Bayesian clustering method in STRUCTURE. The uniqueness of the populations was also supported by private alleles. This indicated a significant population fragmentation in Minshan A region. In addition, the high individual inbreeding coefficients for Wanglang indicated increased levels of homozygosity in the wild populations. Fortunately, those populations had high levels of genetic diversity. The average allelic richness (AR) and expected heterzygosity (H E ) were 4.520 and 0.689, respectively for Tangjiahe and 4.584 and 0.648 for Wanglang. Here, we propose an effective way to restore gene flow between the two isolated populations.

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Detecting genotyping errors and describing American black bear movement in northern Idaho

Abstract: BioOne Complete (complete.BioOne.org) is a full-text database of 200 subscribed and open-access titles in the biological, ecological, and environmental sciences published by nonprofit societies, associations, museums, institutions, and presses.

Cited by 48 publications

References 45 publications

Why sampling scheme matters: the effect of sampling scheme on landscape genetic results

Why sampling scheme matters: the effect of sampling scheme on landscape genetic results

Noninvasive genetic assessment of the population trend and sex ratio of the Shennongjia population of Sichuan snub-nosed monkeys (Rhinopithecus roxellana)

Microsatellite variability reveals significant genetic differentiation of giant pandas (Ailuropoda melanoleuca) in the Minshan A habitat

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