Evidence map›Paper›PMID 41527150›Full record

ArticleMovement ecology2026

Do GPS collars and coded neckbands tell the same story about year-round movements in geese?

Mariëlle L van Toor, Christen H Fleming, Niklas Liljebäck, Johan Månsson, Jonas Waldenström, Johan Elmberg

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Article in Movement ecology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Mariëlle L van ToorCentre for Ecology and Evolution in Microbial Model Systems, Linnaeus University, Kalmar, Sweden. marielle.vantoor@lnu.se.
Christen H FlemingDepartment of Biology, University of Central Florida, Orlando, FL, USA.
Niklas LiljebäckGrimsö Wildlife Research Station, Department of Ecology, Swedish University of Agricultural Sciences, Riddarhyttan, Sweden.
Johan MånssonGrimsö Wildlife Research Station, Department of Ecology, Swedish University of Agricultural Sciences, Riddarhyttan, Sweden.
Jonas WaldenströmCentre for Ecology and Evolution in Microbial Model Systems, Linnaeus University, Kalmar, Sweden.
Johan ElmbergDepartment of Environmental Science, Kristianstad University, Kristianstad, Sweden.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGPS telemetry has become the norm for the tracking of large-bodied bird species, whereas management and conservation of populations often rely on low-tech methods such as capture-mark-resighting (CMR). Direct evaluations of the comparability of the respective outcome from these methods remain rare despite being crucial for comparative studies and management decisions. Here, we investigated whether GPS tracking and CMR lead to same conclusions about seasonal migration and year-round space use. We chose greylag geese (Anser anser) as a study species, for which a long record of both coded neckband reports and GPS tracking are available, and whose management relies on CMR data.

methodsOur data set was comprised of neckband reports and GPS tracks collected for birds from five capture sites in Sweden (n = 665 neckband birds; n = 156 GPS collar birds). We evaluated the similarity of movement metrics and year-round space use derived from continuous-time movement models and auto-correlated kernel density estimators. We further quantified overlap of spatial range estimates between tracking methods for the breeding period and the wintering season. We approximated spatial observation bias by contrasting range estimates estimated with and without the use of a debiasing algorithm.

resultsWe found that estimates of space use derived from CMR and GPS tracking were in general agreement: average year-round space use for most individuals was similar even if means among tracking methods differed among all individuals per method (CMR: [Formula: see text]; GPS: [Formula: see text]), and mean overlap of range estimates for summer and winter did not differ depending on whether the comparison was with the same, or differing tracking methods. Movement metrics differed considerably between methods whenever the CMR data captured behaviour at a different temporal scale than GPS (position & velocity autocorrelation), and else in agreement with GPS tracking (periodicity).

conclusionOur study suggests that the historical and current use of coded neckband data for greylag goose management decisions is appropriate regarding space use of migratory greylag geese in Europe. Understanding whether the existing reporter network can capture changes to the migratory behaviour of greylag geese including short-stopping of migration will however require additional in-depth analyses.

Indexed as

Animal telemetryAuto-correlated kernel density estimatorsBrantaCapture mark recapture/resightingComparative studyGreylag goose Anser anserSeasonal migration

Identifiers

PMID41527150
PMCPMC12829013

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.