Combining genetic non-invasive sampling with spatially explicit capture-recapture models for density estimation of a patchily distributed small mammal

  • Helena Sabino-MarquesEmail author
  • Clara Mendes Ferreira
  • Joana Paupério
  • Pedro Costa
  • Soraia Barbosa
  • Cláudia Encarnação
  • Russell Alpizar-Jara
  • Paulo Célio Alves
  • Jeremy B. Searle
  • António Mira
  • Pedro Beja
  • Ricardo Pita
Original Article


Estimating the size of animal populations is essential for understanding the demography and conservation status of species. Genetic Non-Invasive Sampling (gNIS) combined with Spatially Explicit Capture-Recapture (SECR) modelling may provide a practical tool to obtain such estimates. Here, we evaluate for the first time the potential and limitations of this approach to estimate population densities for small mammals inhabiting patchily distributed habitats, focusing on the endemic Iberian Cabrera vole (Microtus cabrerae). Using 11 highly polymorphic microsatellites and two sex-linked introns, we compared population estimates in November/December 2011 based on live-trapping and gNIS and assessed the impact of distinct consensus criteria to differentiate unique genotypes. Live-trapping over 21 days captured 31 individuals, while gNIS over 5 days recorded 65–69 individuals. SECR models indicated that individual detectability was positively affected by live-trapping capture success on the previous occasion, while for gNIS, it was mainly affected by genotyping success rates and patch size. Live-trapping produced the lowest density estimates (mean ± SE) of 16.6 ± 3.2 individuals per hectare of suitable habitat (ind/ha). Estimates based on gNIS were higher and varied slightly between 25.2 ± 4.0 and 28.8 ± 4.5 ind/ha depending on assuming one or two genotyping errors, respectively, when differentiating individual genetic profiles. Results suggest that live-trapping underestimated the vole population, while the larger number of individuals detected through gNIS allowed better estimates with lower field effort. Overall, we suggest that gNIS combined with SECR models provides an effective tool to estimate small mammal population densities in fragmented habitats.


Cabrera vole SECR model Population biology Population size estimates Fragmented habitats Faecal DNA 



This study was funded by FEDER through the Programa Operacional Factores de Competitividade—COMPETE and the Portuguese Foundation for Science and Technology—FCT—within the scope of the projects PERSIST (PTDC/BIA-BEC/105110/2008), NETPERSIST (PTDC/AAG-MAA/3227/2012) and MateFrag (PTDC/BIA-BIC/6582/2014). HSM was supported by the FCT grant SFRH/BD/73765/2010. JP was supported by a postdoctoral grant funded by the project ‘Genomics and Evolutionary Biology’ co-financed by North Portugal Regional Operational Programme 2007/2013 (ON.2 - O Novo Norte), under the National Strategic Reference Framework, through the ERDF. PB was supported by EDP Biodiversity Chair. RP was supported by the FCT grants SFRH/BPD/73478/2010 and SFRH/BPD/109235/2015. We thank Estrela Matilde for her assistance in fieldwork. We are grateful to Murray Efford for guidance through the secr package and, together with Tiago Marques, for advice regarding data analysis. Ana Galantinho provided useful feedback on a previous version of the manuscript. We also thank two anonymous reviewers who provided valuable suggestions to improve the manuscript.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All applicable international, national and/or institutional guidelines for the care and use of animals were followed. All procedures were carried out under permission from the Portuguese biodiversity conservation agency (ICNF—Instituto de Conservação da Natureza e das Florestas, permit nos. 76, 77 and 80/2011/CAPT) and conformed to the guidelines approved by the American Society of Mammalogists (Sikes et al. 2011).

Supplementary material

10344_2018_1206_MOESM1_ESM.pdf (369 kb)
ESM 1 (PDF 369 kb)


  1. AEMET, IM (2011) Atlas climático Ibérico / Iberian climate atlas. Agencia Estatal de Meteorología, Ministerio de Medio Ambiente y Rural y Marino, Madrid. Instituto de Meteorologia de PortugalGoogle Scholar
  2. Alasaad S, Sánchez A, Marchal JA, Píriz A, Garrido-García JA, Carro F, Romero I, Soriguer RC (2011) Efficient identification of Microtus cabrerae excrements using noninvasive molecular analysis. Conserv Genet Resour 3:127–129. CrossRefGoogle Scholar
  3. Arandjelovic M, Vigilant L (2018) Non-invasive genetic censusing and monitoring of primate populations. Am J Primatol 80:e22743. CrossRefPubMedGoogle Scholar
  4. Barbosa S, Pauperio J, Searle JB, Alves PC (2013) Genetic identification of Iberian rodent species using both mitochondrial and nuclear loci: application to noninvasive sampling. Mol Ecol Resour 13:43–56. CrossRefPubMedGoogle Scholar
  5. Beja-Pereira A, Oliveira R, Alves PC, Schwartz MK, Luikart G (2009) Advancing ecological understandings through technological transformations in noninvasive genetics. Mol Ecol Resour 9:1279–1301. CrossRefPubMedGoogle Scholar
  6. 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 13:3261–3273. CrossRefPubMedGoogle Scholar
  7. Borchers DL, Efford MG (2008) Spatially explicit maximum likelihood methods for capture-recapture studies. Biometrics 64:377–385. CrossRefPubMedGoogle Scholar
  8. Bowers MA, Matter SF (1997) Landscape ecology of mammals: relationships between density and patch-size. J Mammal 78:999–1013. CrossRefGoogle Scholar
  9. Brazeal JL, Weist T, Sacks BN (2017) Noninvasive genetic spatial capture-recapture for estimating deer population abundance. J Wildl Manag 81:629–640. CrossRefGoogle Scholar
  10. Brinkman TJ, Schwartz MK, Person DK, Pilgrim KL, Hundertmark KJ (2010) Effects of time and rainfall on PCR success using DNA extracted from deer fecal pellets. Conserv Genet 11:1547–1552. CrossRefGoogle Scholar
  11. Burnham KP, Anderson DR (2002) Model selection and multimodel inference: a practical information-theoretic approach, 2nd edn. Springer-Verlag, New YorkGoogle Scholar
  12. Cheng E, Hodges KE, Sollmann R, Mills LS (2017) Genetic sampling for estimating density of common species. Ecol Evol 7:6210–6219. CrossRefPubMedPubMedCentralGoogle Scholar
  13. Conroy MJ, Runge JP, Barker RJ, Schofield MR, Fonnesbeck CJ (2008) Efficient estimation of abundance for patchily distributed populations via two-phase, adaptive sampling. Ecology 89:3362–3370. CrossRefPubMedGoogle Scholar
  14. De Barba M, Miquel C, Lobréaux S, Quenette PY, Swenson JE, Taberlet P (2017) High-throughput microsatellite genotyping in ecology: improved accuracy, efficiency, standardization and success with low-quantity and degraded DNA. Mol Ecol Resour 17:492–507. CrossRefPubMedGoogle Scholar
  15. Do R, Shonfield J, McAdam AG (2013) Reducing accidental shrew mortality associated with small-mammal livetrapping II: a field experiment with bait supplementation. J Mammal 94:754–760. CrossRefGoogle Scholar
  16. Efford M (2004) Density estimation in live-trapping studies. Oikos 106:598–610. CrossRefGoogle Scholar
  17. Efford MG (2011) Estimation of population density by spatially explicit capture-recapture analysis of data from area searches. Ecology 92:2202–2207. CrossRefPubMedGoogle Scholar
  18. Efford M (2014) secr: Spatially explicit capture-recapture models. R package version 2.8.2 Accessed 30 April 2014
  19. Efford M (2018) Polygon and transect detectors in secr 3.1. Accessed 16 March 2018
  20. Efford MG, Fewster RM (2013) Estimating population size by spatially explicit capture-recapture. Oikos 122:918–928. CrossRefGoogle Scholar
  21. Efford MG, Borchers DL, Byrom AE (2009) Density estimation by spatially explicit capture–recapture: likelihood-based methods. In: Thomson DL, Cooch EG, Conroy MJ (eds) Modeling demographic processes in marked populations. Springer, Boston, pp 255–269. CrossRefGoogle Scholar
  22. Fernández-Salvador R, Ventura J, García-Perea R (2005) Breeding patterns and demography of a population of the Cabrera vole, Microtus cabrerae. Anim Biol 55:147–161. CrossRefGoogle Scholar
  23. Ferreira CM, Sabino-Marques H, Paupério J, Barbosa S, Costa P, Encarnação C, Alpizar-Jara R, Pita R, Beja P, Mira A, Searle JB, Alves PC (2018) Genetic non-invasive sampling (gNIS) as a cost-effective tool for monitoring elusive small mammals. Eur J Wildl Res in-press.
  24. Fletcher QE, Boonstra R (2006) Impact of live trapping on the stress response of the meadow vole (Microtus pennsylvanicus). J Zool 270:473–478. CrossRefGoogle Scholar
  25. Garrido-García JA, Soriguer RC (2014) Topillo de Cabrera Iberomys cabrerae (Thomas, 1906) In: Calzada J, Clavero M, Fernández A. (eds). Guía virtual de los indicios de los mamíferos de la Península Ibérica, Islas Baleares y Canarias. Sociedad Española para la Conservación y Estudio de los Mamíferos (SECEM). Accessed 20 April 2016
  26. Johannesen E, Andreassen HP, Ims RA (2000) Spatial explicit demography: the effects habitat patch isolation have on vole matrilines. Ecol Lett 3:48–57. CrossRefGoogle Scholar
  27. Johnson PCD, Haydon DT (2007) Maximum-likelihood estimation of allelic dropout and false allele error rates from microsatellite genotypes in the absence of reference data. Genetics 175:827–842. CrossRefPubMedPubMedCentralGoogle Scholar
  28. Kalinowski ST, Sawaya MA, Taper ML (2006) Individual identification and distribution of genotypic differences between individuals. J Wildl Manag 70:1148–1150.[1148:IIADOG]2.0.CO;2Google Scholar
  29. Kéry M, Gardner B, Stoeckle T, Weber D, Royle JA (2010) Use of spatial capture–recapture modeling and DNA data to estimate densities of elusive animals. Conserv Biol 25:356–364. PubMedCrossRefGoogle Scholar
  30. 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 Manag 77:1490–1511. CrossRefGoogle Scholar
  31. Landete-Castillejos T, Andrés-Abellán M, Argandoña JJ, Garde J (2000) Distribution of the Cabrera vole in its first reported areas reassessed by live-trapping. Biol Conserv 94:127–130. CrossRefGoogle Scholar
  32. López-Bao JV, Godinho R, Pacheco C, Lema FJ, García E, Llaneza L, Palacios V, Jiménez J (2018) Toward reliable population estimates of wolves by combining spatial capture-recapture models and non-invasive DNA monitoring. Sci Rep 8:2177. CrossRefPubMedPubMedCentralGoogle Scholar
  33. Lounsberry ZT, Forrester TD, Olegario MT, Brazeal JL, Wittmer HU, Sacks BN (2015) Estimating sex-specific abundance in fawning areas of a high-density Columbian black-tailed deer population using fecal DNA. J Wildl Manag 79:39–49. CrossRefGoogle Scholar
  34. 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 11:355–373. CrossRefGoogle Scholar
  35. Lukacs PM, Burnham KP (2005) Review of capture-recapture methods applicable to noninvasive genetic sampling. Mol Ecol 14:3909–3919. CrossRefPubMedGoogle Scholar
  36. Macbeth GM, Broderick D, Ovenden JR, Buckworth RC (2011) Likelihood-based genetic mark–recapture estimates when genotype samples are incomplete and contain typing errors. Theor Popul Biol 80:185–196. CrossRefPubMedGoogle Scholar
  37. MacGregor-Fors I, Payton ME (2013) Contrasting diversity values: statistical inferences based on overlapping confidence intervals. PLoS One 8:e56794. CrossRefPubMedPubMedCentralGoogle Scholar
  38. Manly BFJ (2004) Two-phase adaptive stratified sampling. In: Thompson WL (ed) Sampling rare or elusive species: concepts, designs, and techniques for estimating population parameters. Island Press, Washington DC, pp 123–133Google Scholar
  39. McCravy KW, Rose RK (1992) An analysis of external features as predictors of reproductive status in small mammals. J Mammal 73:151–159. CrossRefGoogle Scholar
  40. McKelvey KS, Schwartz MK (2004) Genetic errors associated with population estimation using non-invasive molecular tagging: problems and new solutions. J Wildl Manag 68:439–448.[0439,GEAWPE]2.0.CO;2Google Scholar
  41. Mills LS, Citta JJ, Lair KP, Schwarz MK, Tallman DA (2000) Estimating animal abundance using noninvasive DNA sampling: promise and pitfalls. Ecol Appl 10:283–294.[0283:EAAUND]2.0.CO;2Google Scholar
  42. Mollet P, Kéry M, Gardner B, Pasinelli G, Royle JA (2015) Estimating population size for capercaillie (Tetrao urogallus L.) with spatial capture-recapture models based on genotypes from one field sample. PLoS One 10:e0129020. CrossRefPubMedPubMedCentralGoogle Scholar
  43. Mondol S, Karanth KU, Kumar NS, Gopalaswamy AM, Andheria A, Ramakrishnan U (2009) Evaluation of non-invasive genetic sampling methods for estimating tiger population size. Biol Conserv 142:2350–2360. CrossRefGoogle Scholar
  44. Morin DJ, Waits LP, McNitt DC, Kelly MJ (2018) Efficient single-survey estimation of carnivore density using fecal DNA and spatial capture-recapture: a bobcat case study. Popul Ecol 60:197–209. CrossRefGoogle Scholar
  45. Murphy MA, Kendall KC, Robinson A, Waits LP (2007) The impact of time and field conditions on brown bear (Ursus arctos) faecal DNA amplification. Conserv Genet 8:1219–1224. CrossRefGoogle Scholar
  46. Murphy SM, Augustine BC, Ulrey WA, Guthrie JM, Scheick BK, McCown JW, Cox JJ (2017) Consequences of severe habitat fragmentation on density, genetics, and spatial capture-recapture analysis of a small bear population. PLoS One 12:e0181849. CrossRefPubMedPubMedCentralGoogle Scholar
  47. Okello JBA, Wittemyer G, Rasmussen HB, Douglas-Hamilton I, Nyakaana S, Arctander P, Siegismund HR (2005) Noninvasive genotyping and Mendelian analysis of microsatellites in African savannah elephants. J Hered 96:679–687. CrossRefPubMedGoogle Scholar
  48. Piñero FS, Garrido-García JA, Soriguer RC (2012) Dung beetles (Scarabaeidae, Coleoptera) of latrines of the Iberian endemic rodent Microtus cabrerae (Rodentia: Cricetidae: Microtinae) at Sierra de Segura (S. Iberian Peninsula). Bol Asoc Esp Entomol 36:451–455Google Scholar
  49. Pita R, Beja P, Mira A (2007) Spatial population structure of the Cabrera vole in Mediterranean farmland: the relative role of patch and matrix effects. Biol Conserv 134:383–392. CrossRefGoogle Scholar
  50. Pita R, Mira A, Beja P (2010) Spatial segregation of two vole species (Arvicola sapidus and Microtus cabrerae) within habitat patches in a highly fragmented farmland landscape. Eur J Wildl Res 56:651–662. CrossRefGoogle Scholar
  51. Pita R, Mira A, Beja P (2011) Assessing habitat differentiation between coexisting species: the role of spatial scale. Acta Oecol 37:124–132. CrossRefGoogle Scholar
  52. Pita R, Mira A, Beja P (2014) Microtus cabrerae (Rodentia: Cricetidae). Mamm Species 46(912):48–70. CrossRefGoogle Scholar
  53. Pita R, Lambin X, Mira A, Beja P (2016) Hierarchical spatial segregation of two Mediterranean vole species: the role of patch-network structure and matrix composition. Oecologia 182:253–263. CrossRefPubMedGoogle Scholar
  54. R Development Core Team (2014) R: a language and environment for statistical computing, 3.0. R Foundation for Statistical Computing, Vienna, p 2 Accessed 30 April 2014Google Scholar
  55. Rehnus M, Bollman K (2016) Non-invasive genetic population density estimation of mountain hares (Lepus timidus) in the Alps: systematic or opportunistic sampling? Eur J Wildl Res 62:737–747. CrossRefGoogle Scholar
  56. Rodgers TW, Giacalone J, Heske EJ, Janečka JE, Phillips CA, Schooley RL (2014) Comparison of noninvasive genetics and camera trapping for estimating population density of ocelots (Leopardus pardalis) on Barro Colorado Island, Panama. Trop Conserv Sci 7:690–705. CrossRefGoogle Scholar
  57. Rosário IT (2012) Towards a conservation strategy for an endangered rodent, the Cabrera vole (Microtus cabrerae Thomas): insights from ecological data. Ph.D. Dissertation, University of LisbonGoogle Scholar
  58. Royle JA, Chandler RB, Sollmann R, Gardner B (2014) Spatial capture-recapture. Academic Press, WalthamGoogle Scholar
  59. Royle JA, Fuller AK, Sutherland C (2018) Unifying population and landscape ecology with spatial capture–recapture. Ecography 41:444–456. CrossRefGoogle Scholar
  60. Santini A, Lucchini V, Fabbri E, Randi E (2007) Ageing and environmental factors affect PCR success in wolf (Canis lupus) excremental DNA samples. Mol Ecol Notes 7:955–961. CrossRefGoogle Scholar
  61. Scharine PD, Nielsen CK, Schauber EM, Rubert L, Crawford JC (2011) Occupancy, detection, and habitat associations of sympatric lagomorphs in early-successional bottomland forests. J Mammal 92:880–890. CrossRefGoogle Scholar
  62. Schwartz MK, Luikart G, Waples RS (2007) Genetic monitoring as a promising tool for conservation and management. Trends Ecol Evol 22:25–33. CrossRefPubMedGoogle Scholar
  63. Sikes RS, Gannon WL, the Animal Care and Use Committee of the American Society of Mammalogists (2011) Guidelines of the American Society of Mammalogists for the use of wild mammals in research. J Mammal 92:235–253. CrossRefGoogle Scholar
  64. Slauson KM, Zielinski WJ, Schwartz MK (2017) Ski areas affect Pacific marten movement, habitat use, and density. J Wildl Manag 81:892–904. CrossRefGoogle Scholar
  65. Solberg KH, Bellemain E, Drageset OM, Taberlet P, Swenson JE (2006) An evaluation of field and non-invasive genetic methods to estimate brown bear (Ursus arctos) population size. Biol Conserv 128:158–168. CrossRefGoogle Scholar
  66. Stem C, Margoluis R, Salafsky N, Brown M (2005) Monitoring and evaluation in conservation: a review of trends and approaches. Conserv Biol 19:295–309. CrossRefGoogle Scholar
  67. Taberlet P, Waits LP, Luikart G (1999) Noninvasive genetic sampling: look before you leap. Trends Ecol Evol 14:323–327. CrossRefPubMedGoogle Scholar
  68. Thompson WL (ed) (2004) Sampling rare or elusive species: concepts, designs, and techniques for estimating population parameters. Island Press, Washington DCGoogle Scholar
  69. Thompson CM, Royle JA, Garner JD (2012) A framework for inference about carnivore density from unstructured spatial sampling of scat using detector dogs. J Wildl Manag 76:863–871. CrossRefGoogle Scholar
  70. Traill LW, Bradshaw CJA, Brook BW (2007) Minimum viable population size: a meta-analysis of 30 years of published estimates. Biol Conserv 139:159–166. CrossRefGoogle Scholar
  71. Turner MG, Gardner RH, O’Neill RV (2001) Landscape ecology in theory and practice: pattern and process. Springer-Verlag, New YorkGoogle Scholar
  72. Valière N (2002) GIMLET: a computer program for analysing genetic individual identification data. Mol Ecol Notes 2:377–379. CrossRefGoogle Scholar
  73. Waits JL, Leberg PL (2000) Biases associated with population estimation using molecular tagging. Anim Conserv 3:191–199. CrossRefGoogle Scholar
  74. Waits LP, Luikart G, Taberlet P (2001) Estimating the probability of identity among genotypes in natural populations: cautions and guidelines. Mol Ecol 10:249–256. CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Helena Sabino-Marques
    • 1
    • 2
    Email author
  • Clara Mendes Ferreira
    • 3
    • 4
  • Joana Paupério
    • 3
  • Pedro Costa
    • 1
    • 2
  • Soraia Barbosa
    • 3
    • 4
    • 5
  • Cláudia Encarnação
    • 1
    • 2
  • Russell Alpizar-Jara
    • 6
  • Paulo Célio Alves
    • 3
    • 4
    • 7
  • Jeremy B. Searle
    • 3
    • 5
  • António Mira
    • 1
    • 2
  • Pedro Beja
    • 3
    • 8
  • Ricardo Pita
    • 1
    • 2
    • 8
  1. 1.CIBIO/InBio-UE—Centro de Investigação em Biodiversidade e Recursos Genéticos, Pólo de ÉvoraUniversidade de ÉvoraÉvoraPortugal
  2. 2.Unidade de Biologia da Conservação, Departamento de BiologiaUniversidade de ÉvoraÉvoraPortugal
  3. 3.CIBIO/InBio—Centro de Investigação em Biodiversidade e Recursos GenéticosUniversidade do PortoVairãoPortugal
  4. 4.Departamento de BiologiaFaculdade de Ciências da Universidade do PortoPortoPortugal
  5. 5.Department of Ecology and Evolutionary BiologyCornell UniversityIthacaUSA
  6. 6.CIMA—Centro de Investigação em Matemática e Aplicações, IIFA, Departamento de Matemática, Escola de Ciências e TecnologiaUniversidade de ÉvoraÉvoraPortugal
  7. 7.Wildlife Biology Program, Department of Ecosystem and Conservation SciencesUniversity of MontanaMissoulaUSA
  8. 8.CEABN/InBIO—Centro de Ecologia Aplicada “Professor Baeta Neves”, Instituto Superior de AgronomiaUniversidade de LisboaLisboaPortugal

Personalised recommendations