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Bayesian N-Mixture Models Applied to Estimating Insect Abundance

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Bayesian N-Mixture Models Applied to Estimating Insect Abundance. / Mimnagh, Niamh; Parnell, Andrew; Batista Do Prado, Estevao.
Modelling Insect Populations in Agricultural Landscapes . ed. / Rafael A. Moral; Wesley A.C. Godoy. 1. ed. Cham: Springer, 2023. p. 185-210 (Entomology in Focus; Vol. 8).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter

Harvard

Mimnagh, N, Parnell, A & Batista Do Prado, E 2023, Bayesian N-Mixture Models Applied to Estimating Insect Abundance. in RA Moral & WAC Godoy (eds), Modelling Insect Populations in Agricultural Landscapes . 1 edn, Entomology in Focus, vol. 8, Springer, Cham, pp. 185-210. https://doi.org/10.1007/978-3-031-43098-5_10

APA

Mimnagh, N., Parnell, A., & Batista Do Prado, E. (2023). Bayesian N-Mixture Models Applied to Estimating Insect Abundance. In R. A. Moral, & W. A. C. Godoy (Eds.), Modelling Insect Populations in Agricultural Landscapes (1 ed., pp. 185-210). (Entomology in Focus; Vol. 8). Springer. https://doi.org/10.1007/978-3-031-43098-5_10

Vancouver

Mimnagh N, Parnell A, Batista Do Prado E. Bayesian N-Mixture Models Applied to Estimating Insect Abundance. In Moral RA, Godoy WAC, editors, Modelling Insect Populations in Agricultural Landscapes . 1 ed. Cham: Springer. 2023. p. 185-210. (Entomology in Focus). doi: 10.1007/978-3-031-43098-5_10

Author

Mimnagh, Niamh ; Parnell, Andrew ; Batista Do Prado, Estevao. / Bayesian N-Mixture Models Applied to Estimating Insect Abundance. Modelling Insect Populations in Agricultural Landscapes . editor / Rafael A. Moral ; Wesley A.C. Godoy. 1. ed. Cham : Springer, 2023. pp. 185-210 (Entomology in Focus).

Bibtex

@inbook{85673187207e422eb1212988109f5a71,
title = "Bayesian N-Mixture Models Applied to Estimating Insect Abundance",
abstract = "Estimating animal abundance is an area of interest for many conservationists and population ecologists. When using count data to obtain abundance estimates, issues of imperfect detection must be taken into account. The N-mixture model proposed by Royle (Biometrics, 60(1), 108–115, 2004) provides a solution to this issue by incorporating detection probability in the estimate of abundance. We examine the original N-mixture model and assess its uses, advantages and assumptions. We then examine extensions to this vanilla N-mixture model which allow for the estimation of abundance using a range of data unsupported by the original model, including data that contain observations of multiple animals, data that contain large numbers of zero-counts and data that are collected over long periods of time. We finish by illustrating the applicability of both the original N-mixture model and a model proposed for multiple species collected over several years (Mimnagh et al., Environmental and Ecological Statistics, 1–24, 2022) by estimating foraging populations for bee species from data collected in 2016 and 2019 as part of the BeeWalk Survey in the UK.",
author = "Niamh Mimnagh and Andrew Parnell and {Batista Do Prado}, Estevao",
year = "2023",
month = aug,
day = "10",
doi = "10.1007/978-3-031-43098-5_10",
language = "English",
isbn = "9783031430978",
series = "Entomology in Focus",
publisher = "Springer",
pages = "185--210",
editor = "Moral, {Rafael A.} and Godoy, {Wesley A.C.}",
booktitle = "Modelling Insect Populations in Agricultural Landscapes",
edition = "1",

}

RIS

TY - CHAP

T1 - Bayesian N-Mixture Models Applied to Estimating Insect Abundance

AU - Mimnagh, Niamh

AU - Parnell, Andrew

AU - Batista Do Prado, Estevao

PY - 2023/8/10

Y1 - 2023/8/10

N2 - Estimating animal abundance is an area of interest for many conservationists and population ecologists. When using count data to obtain abundance estimates, issues of imperfect detection must be taken into account. The N-mixture model proposed by Royle (Biometrics, 60(1), 108–115, 2004) provides a solution to this issue by incorporating detection probability in the estimate of abundance. We examine the original N-mixture model and assess its uses, advantages and assumptions. We then examine extensions to this vanilla N-mixture model which allow for the estimation of abundance using a range of data unsupported by the original model, including data that contain observations of multiple animals, data that contain large numbers of zero-counts and data that are collected over long periods of time. We finish by illustrating the applicability of both the original N-mixture model and a model proposed for multiple species collected over several years (Mimnagh et al., Environmental and Ecological Statistics, 1–24, 2022) by estimating foraging populations for bee species from data collected in 2016 and 2019 as part of the BeeWalk Survey in the UK.

AB - Estimating animal abundance is an area of interest for many conservationists and population ecologists. When using count data to obtain abundance estimates, issues of imperfect detection must be taken into account. The N-mixture model proposed by Royle (Biometrics, 60(1), 108–115, 2004) provides a solution to this issue by incorporating detection probability in the estimate of abundance. We examine the original N-mixture model and assess its uses, advantages and assumptions. We then examine extensions to this vanilla N-mixture model which allow for the estimation of abundance using a range of data unsupported by the original model, including data that contain observations of multiple animals, data that contain large numbers of zero-counts and data that are collected over long periods of time. We finish by illustrating the applicability of both the original N-mixture model and a model proposed for multiple species collected over several years (Mimnagh et al., Environmental and Ecological Statistics, 1–24, 2022) by estimating foraging populations for bee species from data collected in 2016 and 2019 as part of the BeeWalk Survey in the UK.

U2 - 10.1007/978-3-031-43098-5_10

DO - 10.1007/978-3-031-43098-5_10

M3 - Chapter

SN - 9783031430978

T3 - Entomology in Focus

SP - 185

EP - 210

BT - Modelling Insect Populations in Agricultural Landscapes

A2 - Moral, Rafael A.

A2 - Godoy, Wesley A.C.

PB - Springer

CY - Cham

ER -