Skip to content

Research at St Andrews

Cluster capture-recapture to account for identification uncertainty on aerial surveys of animal populations

Research output: Contribution to journalArticle

DOI

Standard

Cluster capture-recapture to account for identification uncertainty on aerial surveys of animal populations. / Stevenson, Ben C.; Borchers, David L.; Fewster, Rachel M.

In: Biometrics, Vol. 75, No. 1, 2019, p. 326-336.

Research output: Contribution to journalArticle

Harvard

Stevenson, BC, Borchers, DL & Fewster, RM 2019, 'Cluster capture-recapture to account for identification uncertainty on aerial surveys of animal populations' Biometrics, vol. 75, no. 1, pp. 326-336. https://doi.org/10.1111/biom.12983

APA

Stevenson, B. C., Borchers, D. L., & Fewster, R. M. (2019). Cluster capture-recapture to account for identification uncertainty on aerial surveys of animal populations. Biometrics, 75(1), 326-336. https://doi.org/10.1111/biom.12983

Vancouver

Stevenson BC, Borchers DL, Fewster RM. Cluster capture-recapture to account for identification uncertainty on aerial surveys of animal populations. Biometrics. 2019;75(1):326-336. https://doi.org/10.1111/biom.12983

Author

Stevenson, Ben C. ; Borchers, David L. ; Fewster, Rachel M. / Cluster capture-recapture to account for identification uncertainty on aerial surveys of animal populations. In: Biometrics. 2019 ; Vol. 75, No. 1. pp. 326-336.

Bibtex - Download

@article{f11adcc5d9ec43fe87b7fbb6874fe5fa,
title = "Cluster capture-recapture to account for identification uncertainty on aerial surveys of animal populations",
abstract = "Capture‐recapture methods for estimating wildlife population sizes almost always require their users to identify every detected animal. Many modern‐day wildlife surveys detect animals without physical capture—visual detection by cameras is one such example. However, for every pair of detections, the surveyor faces a decision that is often fraught with uncertainty: are they linked to the same individual? An inability to resolve every such decision to a high degree of certainty prevents the use of standard capture‐recapture methods, impeding the estimation of animal density. Here, we develop an estimator for aerial surveys, on which two planes or unmanned vehicles (drones) fly a transect over the survey region, detecting individuals via high‐definition cameras. Identities remain unknown, so one cannot discern if two detections match to the same animal; however, detections in close proximity are more likely to match. By modeling detection locations as a clustered point process, we extend recently developed methodology and propose a precise and computationally efficient estimator of animal density that does not require individual identification. We illustrate the method with an aerial survey of porpoise, on which cameras detect individuals at the surface of the sea, and we need to take account of the fact that they are not always at the surface.",
keywords = "Capture-recapture, Neyman-scott process, Palm intensity, Spatial capture-recapture, Thomas process, Unmanned aerial vehicles",
author = "Stevenson, {Ben C.} and Borchers, {David L.} and Fewster, {Rachel M.}",
note = "This work was funded by a joint EPSRC/NERC PhD grant (No. EP/1000917/1), by the EPSRC through a Doctoral Prize Fellowship, and by the Royal Society of New Zealand through Marsden grant 14-UOA-155.",
year = "2019",
doi = "10.1111/biom.12983",
language = "English",
volume = "75",
pages = "326--336",
journal = "Biometrics",
issn = "0006-341X",
publisher = "Wiley",
number = "1",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Cluster capture-recapture to account for identification uncertainty on aerial surveys of animal populations

AU - Stevenson, Ben C.

AU - Borchers, David L.

AU - Fewster, Rachel M.

N1 - This work was funded by a joint EPSRC/NERC PhD grant (No. EP/1000917/1), by the EPSRC through a Doctoral Prize Fellowship, and by the Royal Society of New Zealand through Marsden grant 14-UOA-155.

PY - 2019

Y1 - 2019

N2 - Capture‐recapture methods for estimating wildlife population sizes almost always require their users to identify every detected animal. Many modern‐day wildlife surveys detect animals without physical capture—visual detection by cameras is one such example. However, for every pair of detections, the surveyor faces a decision that is often fraught with uncertainty: are they linked to the same individual? An inability to resolve every such decision to a high degree of certainty prevents the use of standard capture‐recapture methods, impeding the estimation of animal density. Here, we develop an estimator for aerial surveys, on which two planes or unmanned vehicles (drones) fly a transect over the survey region, detecting individuals via high‐definition cameras. Identities remain unknown, so one cannot discern if two detections match to the same animal; however, detections in close proximity are more likely to match. By modeling detection locations as a clustered point process, we extend recently developed methodology and propose a precise and computationally efficient estimator of animal density that does not require individual identification. We illustrate the method with an aerial survey of porpoise, on which cameras detect individuals at the surface of the sea, and we need to take account of the fact that they are not always at the surface.

AB - Capture‐recapture methods for estimating wildlife population sizes almost always require their users to identify every detected animal. Many modern‐day wildlife surveys detect animals without physical capture—visual detection by cameras is one such example. However, for every pair of detections, the surveyor faces a decision that is often fraught with uncertainty: are they linked to the same individual? An inability to resolve every such decision to a high degree of certainty prevents the use of standard capture‐recapture methods, impeding the estimation of animal density. Here, we develop an estimator for aerial surveys, on which two planes or unmanned vehicles (drones) fly a transect over the survey region, detecting individuals via high‐definition cameras. Identities remain unknown, so one cannot discern if two detections match to the same animal; however, detections in close proximity are more likely to match. By modeling detection locations as a clustered point process, we extend recently developed methodology and propose a precise and computationally efficient estimator of animal density that does not require individual identification. We illustrate the method with an aerial survey of porpoise, on which cameras detect individuals at the surface of the sea, and we need to take account of the fact that they are not always at the surface.

KW - Capture-recapture

KW - Neyman-scott process

KW - Palm intensity

KW - Spatial capture-recapture

KW - Thomas process

KW - Unmanned aerial vehicles

U2 - 10.1111/biom.12983

DO - 10.1111/biom.12983

M3 - Article

VL - 75

SP - 326

EP - 336

JO - Biometrics

T2 - Biometrics

JF - Biometrics

SN - 0006-341X

IS - 1

ER -

Related by author

  1. Open population maximum likelihood spatial capture-recapture

    Glennie, R., Borchers, D. L., Murchie, M., Harmsen, B. J. & Foster, R. J., 25 Jul 2019, In : Biometrics. Early View, 11 p.

    Research output: Contribution to journalArticle

  2. inlabru: an R package for Bayesian spatial modelling from ecological survey data

    Bachl, F. E., Lindgren, F., Borchers, D. L. & Illian, J. B., Jun 2019, In : Methods in Ecology and Evolution. 10, 6, p. 760-766 7 p.

    Research output: Contribution to journalArticle

  3. Point process models for spatio-temporal distance sampling data from a large-scale survey of blue whales

    Yuan, Y., Bachl, F. E., Lindgren, F., Borchers, D. L., Illian, J. B., Buckland, S. T., Rue, H. & Gerrodette, T., Dec 2017, In : Annals of Applied Statistics. 11, 4, p. 2270-2297

    Research output: Contribution to journalArticle

  4. Guest editors’ introduction to the special issue on “Ecological Statistics”

    Langrock, R. & Borchers, D. L., 1 Oct 2017, In : AStA Advances in Statistical Analysis. 101, 4, p. 345-347 3 p.

    Research output: Contribution to journalEditorial

  5. From distance sampling to spatial capture-recapture

    Borchers, D. L. & Marques, T. A., Oct 2017, In : Advances in Statistical Analysis. 101, 4, p. 475-494 20 p.

    Research output: Contribution to journalArticle

Related by journal

  1. Biometrics (Journal)

    Hannah Worthington (Reviewer)
    Apr 2018

    Activity: Publication peer-review and editorial work typesPeer review of manuscripts

  2. Biometrics (Journal)

    Hannah Worthington (Reviewer)
    Jan 2017

    Activity: Publication peer-review and editorial work typesPeer review of manuscripts

  3. Biometrics (Journal)

    Stephen Terrence Buckland (Editor)
    20052011

    Activity: Publication peer-review and editorial work typesEditor of research journal

  4. Biometrics (Journal)

    David Louis Borchers (Editor)
    2001 → …

    Activity: Publication peer-review and editorial work typesEditor of research journal

Related by journal

  1. Open population maximum likelihood spatial capture-recapture

    Glennie, R., Borchers, D. L., Murchie, M., Harmsen, B. J. & Foster, R. J., 25 Jul 2019, In : Biometrics. Early View, 11 p.

    Research output: Contribution to journalArticle

  2. Distance sampling detection functions: 2D or not 2D?

    Borchers, D. L. & Cox, M. J., 15 Jun 2017, In : Biometrics. 73, 2, p. 593-602 10 p.

    Research output: Contribution to journalArticle

  3. Double-observer line transect surveys with Markov-modulated Poisson process models for overdispersed animal availability

    Borchers, D. L. & Langrock, R., Dec 2015, In : Biometrics. 71, 4, p. 1060-1069 10 p.

    Research output: Contribution to journalArticle

  4. Nonparametric inference in hidden Markov models using P-splines

    Langrock, R., Kneib, T., Sohn, A. & De Ruiter, S. L., 2015, In : Biometrics. 71, 2, p. 520-528

    Research output: Contribution to journalArticle

ID: 255284445