Using integrated population models for insights into monitoring programs: an application using pink-footed geese

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Using integrated population models for insights into monitoring programs : an application using pink-footed geese. / Johnson, Fred A.; Zimmerman, Guthrie S.; Jensen, Gitte Høj; Clausen, Kevin Kuhlmann; Frederiksen, Morten; Madsen, Jesper.

In: Ecological Modelling, Vol. 415, 108869, 2020.

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@article{f228f0bfe86e452d8de4576c15ece592,
title = "Using integrated population models for insights into monitoring programs: an application using pink-footed geese",
abstract = "Development of integrated population models (IPMs) assume the absence of systematic bias in monitoring programs, yet many potential sources of systematic bias in monitoring data exist (e.g., under-counts of abundance). By integrating multiple sources of data, we can assess whether various sources of monitoring data provide consistent inferences about changes in population size and, thus, whether monitoring programs appear unbiased. For the purposes of understanding how IPMs could provide insights for monitoring programs, we used the Svalbard breeding population of pink-footed goose (Anser brachyrhynchus) as a case study. The Svalbard pink-footed goose is a well-studied species, the focus of the first adaptive-harvest-management program in Europe, and the subject of a variety of long-term monitoring programs. We examined two formulations of an IPM, but ultimately relied on the one that provided a satisfactory fit to all the available data as based on Chi-squared goodness of fit tests. Our analyses suggest a negative bias in November counts (-20 {\%}), a negative bias in capture-mark-recapture estimates of survival (-3 {\%}), and a negative bias in indices of productivity (-23 {\%}). We offer possible explanations for these biases, whether the degree of bias seems reasonable considering those explanations, and how bias might be investigated directly and ultimately avoided or corrected. Finally, we discuss implications of our work for developing IPMs and associated monitoring programs for managing pink-footed geese and other waterbird species.",
author = "Johnson, {Fred A.} and Zimmerman, {Guthrie S.} and Jensen, {Gitte H{\o}j} and Clausen, {Kevin Kuhlmann} and Morten Frederiksen and Jesper Madsen",
year = "2020",
doi = "10.1016/j.ecolmodel.2019.108869",
language = "English",
volume = "415",
journal = "Ecological Modelling",
issn = "0304-3800",
publisher = "Elsevier BV",

}

RIS

TY - JOUR

T1 - Using integrated population models for insights into monitoring programs

T2 - an application using pink-footed geese

AU - Johnson, Fred A.

AU - Zimmerman, Guthrie S.

AU - Jensen, Gitte Høj

AU - Clausen, Kevin Kuhlmann

AU - Frederiksen, Morten

AU - Madsen, Jesper

PY - 2020

Y1 - 2020

N2 - Development of integrated population models (IPMs) assume the absence of systematic bias in monitoring programs, yet many potential sources of systematic bias in monitoring data exist (e.g., under-counts of abundance). By integrating multiple sources of data, we can assess whether various sources of monitoring data provide consistent inferences about changes in population size and, thus, whether monitoring programs appear unbiased. For the purposes of understanding how IPMs could provide insights for monitoring programs, we used the Svalbard breeding population of pink-footed goose (Anser brachyrhynchus) as a case study. The Svalbard pink-footed goose is a well-studied species, the focus of the first adaptive-harvest-management program in Europe, and the subject of a variety of long-term monitoring programs. We examined two formulations of an IPM, but ultimately relied on the one that provided a satisfactory fit to all the available data as based on Chi-squared goodness of fit tests. Our analyses suggest a negative bias in November counts (-20 %), a negative bias in capture-mark-recapture estimates of survival (-3 %), and a negative bias in indices of productivity (-23 %). We offer possible explanations for these biases, whether the degree of bias seems reasonable considering those explanations, and how bias might be investigated directly and ultimately avoided or corrected. Finally, we discuss implications of our work for developing IPMs and associated monitoring programs for managing pink-footed geese and other waterbird species.

AB - Development of integrated population models (IPMs) assume the absence of systematic bias in monitoring programs, yet many potential sources of systematic bias in monitoring data exist (e.g., under-counts of abundance). By integrating multiple sources of data, we can assess whether various sources of monitoring data provide consistent inferences about changes in population size and, thus, whether monitoring programs appear unbiased. For the purposes of understanding how IPMs could provide insights for monitoring programs, we used the Svalbard breeding population of pink-footed goose (Anser brachyrhynchus) as a case study. The Svalbard pink-footed goose is a well-studied species, the focus of the first adaptive-harvest-management program in Europe, and the subject of a variety of long-term monitoring programs. We examined two formulations of an IPM, but ultimately relied on the one that provided a satisfactory fit to all the available data as based on Chi-squared goodness of fit tests. Our analyses suggest a negative bias in November counts (-20 %), a negative bias in capture-mark-recapture estimates of survival (-3 %), and a negative bias in indices of productivity (-23 %). We offer possible explanations for these biases, whether the degree of bias seems reasonable considering those explanations, and how bias might be investigated directly and ultimately avoided or corrected. Finally, we discuss implications of our work for developing IPMs and associated monitoring programs for managing pink-footed geese and other waterbird species.

U2 - 10.1016/j.ecolmodel.2019.108869

DO - 10.1016/j.ecolmodel.2019.108869

M3 - Journal article

VL - 415

JO - Ecological Modelling

JF - Ecological Modelling

SN - 0304-3800

M1 - 108869

ER -