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

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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.
OriginalsprogEngelsk
Artikelnummer108869
TidsskriftEcological Modelling
Vol/bind415
Antal sider13
ISSN0304-3800
DOI
StatusUdgivet - 2020

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