Published

2024-01-01

Addressing Misidentification in Noninvasive DNA Sampling Using Bayesian Approach and Simulations

La identificación errónea en el muestreo de ADN no invasivo abordada mediante modelos bayesianos y simulaciones

DOI:

https://doi.org/10.15446/rce.v47n1.109069

Keywords:

Noninvasive DNA sampling, MCMC, Misidentification, Reversibility, Latent individual (en)
Muestreo de ADN no invasivo, Reversibilidad, Métodos MCMC, Identificación errónea, Individuos latentes (es)

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Authors

  • Paula Bran Universidad del Valle
  • Leon Escobar Universidad del Valle

Noninvasive DNA sampling has become increasingly popular in wildlife research and conservation because it allows scientists to gather valuable genetic information without disturbing or harming the animals. However, the correct identification of the species or individuals in the sample is virtually impossible when using this kind of sampling. Consequently, it becomes essential to consider the errors hidding true identities in order to improve the quality of the data. Errors, if left unaddressed, can have a considerable impact on the accuracy of statistical inferences drawn from the data. This paper endeavours to review some research about misidentification problems and how Bayesian models and Markov Chain Monte Carlo (MCMC) methods can be applied. In addition, a hypothetical scenario is presented to illustrate how genetic material can serve as unique identifier of individuals, and to highlight the potential difficulties that may arise if a proposal distribution for the MCMC simulations is inappropriately chosen.

El muestreo de ADN no invasivo se ha vuelto cada vez más popular en la investigación y conservación de vida silvestre, ya que permite a los científicos recopilar información genética valiosa sin perturbar ni lesionar a los animales. Sin embargo, la correcta identificación de la especie o individuos en la muestra es prácticamente imposible cuando se utiliza este tipo de muestreo. En consecuencia, es fundamental considerar los errores que ocultan las verdaderas identidades con el fin de mejorar la calidad de los datos. Si los errores no se abordan, pueden tener un impacto considerable en la precisión de las inferencias estadísticas obtenidas a partir de los datos. Este artículo se propone revisar algunas investigaciones sobre problemas de identificación errónea y cómo se pueden aplicar los modelos bayesianos y los métodos de Monte Carlo basados en cadenas de Markov (MCMC). Además, se presenta un escenario hipotético para ilustrar cómo el material genético puede servir como identificador único de los individuos, y resaltar las dificultades potenciales que pueden surgir si se elige inapropiadamente una distribución de propuestas para las simulaciones de MCMC.

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How to Cite

APA

Bran, P. and Escobar, L. (2024). Addressing Misidentification in Noninvasive DNA Sampling Using Bayesian Approach and Simulations. Revista Colombiana de Estadística, 47(1), 135–150. https://doi.org/10.15446/rce.v47n1.109069

ACM

[1]
Bran, P. and Escobar, L. 2024. Addressing Misidentification in Noninvasive DNA Sampling Using Bayesian Approach and Simulations. Revista Colombiana de Estadística. 47, 1 (Jan. 2024), 135–150. DOI:https://doi.org/10.15446/rce.v47n1.109069.

ACS

(1)
Bran, P.; Escobar, L. Addressing Misidentification in Noninvasive DNA Sampling Using Bayesian Approach and Simulations. Rev. colomb. estad. 2024, 47, 135-150.

ABNT

BRAN, P.; ESCOBAR, L. Addressing Misidentification in Noninvasive DNA Sampling Using Bayesian Approach and Simulations. Revista Colombiana de Estadística, [S. l.], v. 47, n. 1, p. 135–150, 2024. DOI: 10.15446/rce.v47n1.109069. Disponível em: https://revistas.unal.edu.co/index.php/estad/article/view/109069. Acesso em: 28 mar. 2025.

Chicago

Bran, Paula, and Leon Escobar. 2024. “ Addressing Misidentification in Noninvasive DNA Sampling Using Bayesian Approach and Simulations”. Revista Colombiana De Estadística 47 (1):135-50. https://doi.org/10.15446/rce.v47n1.109069.

Harvard

Bran, P. and Escobar, L. (2024) “ Addressing Misidentification in Noninvasive DNA Sampling Using Bayesian Approach and Simulations”, Revista Colombiana de Estadística, 47(1), pp. 135–150. doi: 10.15446/rce.v47n1.109069.

IEEE

[1]
P. Bran and L. Escobar, “ Addressing Misidentification in Noninvasive DNA Sampling Using Bayesian Approach and Simulations”, Rev. colomb. estad., vol. 47, no. 1, pp. 135–150, Jan. 2024.

MLA

Bran, P., and L. Escobar. “ Addressing Misidentification in Noninvasive DNA Sampling Using Bayesian Approach and Simulations”. Revista Colombiana de Estadística, vol. 47, no. 1, Jan. 2024, pp. 135-50, doi:10.15446/rce.v47n1.109069.

Turabian

Bran, Paula, and Leon Escobar. “ Addressing Misidentification in Noninvasive DNA Sampling Using Bayesian Approach and Simulations”. Revista Colombiana de Estadística 47, no. 1 (January 24, 2024): 135–150. Accessed March 28, 2025. https://revistas.unal.edu.co/index.php/estad/article/view/109069.

Vancouver

1.
Bran P, Escobar L. Addressing Misidentification in Noninvasive DNA Sampling Using Bayesian Approach and Simulations. Rev. colomb. estad. [Internet]. 2024 Jan. 24 [cited 2025 Mar. 28];47(1):135-50. Available from: https://revistas.unal.edu.co/index.php/estad/article/view/109069

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