ConGenR (available at http://www.uidaho.edu/cnr/research-outreach/facilities/leecg/publications-and-software) is an R based conservation genetics script that facilitates rapid determination of consensus genotypes from replicated samples, determines overall (successful amplifications/amplification attempted) and individual sample level (proportion of samples with successful amplifications at n loci) amplification success rates, and quantifies genotyping error rates. ConGenR is intended for use with codominant, multilocus microsatellite data generated primarily through noninvasive genetic sampling and processed with a multi-tubes approach. ConGenR handles input that can be easily exported from GENEMAPPER, a program commonly used to score allele sizes. Amplification success and genotyping error rates can be evaluated by sample class (i.e., any identifiable and meaningful subdivision of samples; e.g., sex, season, region, or sample condition), offering insights into processes driving amplification success and genotyping error rates. Additionally, amplification success and genotyping error rates are calculated by locus, expediting the identification of problematic loci during pilot studies.
This is a preview of subscription content, log in to check access.
This project was funded by the U.S. Department of Defense’s Environmental Security Technology Certification (12 EB-RC5-006) and Legacy Resource Management (W9132T-12-2-0050) programs. We thank K Cleary and S Woodruff for providing data and opportunities to test and improve ConGenR.
Beja-Pereira A, Oliveira R, Alves PC, Schwartz MK, Luikart G (2009) Advancing ecological understandings through technological transformations in noninvasive genetics. Mol Ecol Resour 9:1279–1301CrossRefPubMedGoogle Scholar
Bonin A, Bellemain E, Bronken Eidesen P, Pompanon F, Brochmann C, Taberlet P (2004) How to track and assess genotyping errors in population genetics studies. Mol Ecol 13:3261–3273CrossRefPubMedGoogle Scholar
Brinkman TJ, Person DK, Schwartz MK, Pilgrim KL, Colson KE, Hundertmark KJ (2010) Individual identification of sitka black-tailed deer (Odocoileus hemionus sitkensis) using DNA from fecal pellets. Conserv Genet Resour 2:115–118CrossRefGoogle Scholar
Broquet T, Petit E (2004) Quantifying genotyping errors in noninvasive population genetics. Mol Ecol 13:3601–3608CrossRefPubMedGoogle Scholar
Broquet T, Ménard N, Petit E (2006) Noninvasive population genetics: a review of sample source, diet, fragment length and microsatellite motif effects on amplification success and genotyping error rates. Conserv Genet 8:249–260CrossRefGoogle Scholar
Flagstad Ø, Hedmark E, Landa A, Brøseth H, Persson J, Andersen R, Segerström P, Ellegren H (2004) Colonization history and noninvasive monitoring of a reestablished wolverine population. Conserv Biol 18:676–688CrossRefGoogle Scholar
Frantz AC, Pope LC, Carpenter PJ, Roper TJ, Wilson GJ, Delahay RJ, Burke T (2003) Reliable microsatellite genotyping of the Eurasian badger (Meles meles) using faecal DNA. Mol Ecol 12:1649–1661CrossRefPubMedGoogle Scholar
Kendall KC, Stetz JB, Roon DA, Waits LP, Boulanger JB, Paetkau D (2008) Grizzly bear density in Glacier National Park, Montana. J Wildl Manage 72:1693–1705CrossRefGoogle Scholar
Lampa S, Henle K, Klenke R, Hoehn M, Gruber B (2013) How to overcome genotyping errors in non-invasive genetic mark-recapture population size estimation–a review of available methods illustrated by a case study. J Wildl Manage 77:1490–1511CrossRefGoogle Scholar
Lonsinger RC, Gese EM, Dempsey SJ, Kluever BM, Johnson TR, Waits LP (2015) Balancing sample accumulation and DNA degradation rates to optimize noninvasive genetic sampling of sympatric carnivores. Mol Ecol Resour 15:831–842Google Scholar
Luikart G, Ryman N, Tallmon DA, Schwartz MK, Allendorf FW (2010) Estimation of census and effective population sizes: the increasing usefulness of DNA-based approaches. Conserv Genet 11:355–373CrossRefGoogle Scholar
Marucco F, Boitani L, Pletscher DH, Schwartz MK (2011) Bridging the gaps between non-invasive genetic sampling and population parameter estimation. Eur J Wildl Res 57:1–13CrossRefGoogle Scholar
Pompanon F, Bonin A, Bellemain E, Taberlet P (2005) Genotyping errors: causes, consequences and solutions. Nat Rev Genet 6:847–859CrossRefPubMedGoogle Scholar
R Core Team (2015) R: a language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
Rodgers TW, Janečka JE (2012) Applications and techniques for non-invasive faecal genetics research in felid conservation. Eur J Wildl Res 59:1–16CrossRefGoogle Scholar
Taberlet P, Griffin S, Goossens B, Questiau S, Manceau V, Escaravage N, Waits LP, Bouvet J (1996) Reliable genotyping of samples with very low DNA quantities using PCR. Nucleic Acids Res 24:3189–3194PubMedCentralCrossRefPubMedGoogle Scholar
Valière N, Bonenfant C, Toïgo C, Luikart G, Gaillard J, Klein F (2006) Importance of a pilot study for non-invasive genetic sampling: genotyping errors and population size estimation in red deer. Conserv Genet 8:69–78CrossRefGoogle Scholar
Waits JL, Leberg PL (2000) Biases associated with population estimation using molecular tagging. Anim Conserv 3:191–199CrossRefGoogle Scholar
Waits LP, Paetkau D (2005) Noninvasive genetic sampling tools for wildlife biologists: a review of applications and recommendations for accurate data collection. J Wildl Manage 69:1419–1433CrossRefGoogle Scholar
Waits LP, Luikart G, Taberlet P (2001) Estimating the probability of identity among genotypes in natural populations: cautions and guidelines. Mol Ecol 10:249–256CrossRefPubMedGoogle Scholar