title: Temporal trends of Hg in Arctic biota, an update.
abstract: A statistically robust method was applied to 83 time-series of mercury in Arctic biota from marine, freshwater and terrestrial ecosystems with the purpose of generating a 'meta-analysis' of temporal trend data collected over the past two to three decades, mostly under the auspices of the Arctic Monitoring and Assessment Program (AMAP). Sampling locations ranged from Alaska in the west to northern Scandinavia in the east. Information from recently published temporal trend studies was tabulated to supplement the results of the statistical analyses. No generally consistent trend was evident across tissues and species from the circumpolar Arctic during the last 30years or so. However, there was a clear west-to-east gradient in the occurrence of recent increasing Hg trends, with larger numbers and a higher proportion of biotic datasets in the Canadian and Greenland region of the Arctic showing significant increases than in the North Atlantic Arctic. Most of the increasing datasets were for marine species, especially marine mammals. A total of 16 (19%) out of the 83 time-series could be classified as "adequate", where adequate is defined as the number of actual monitoring years in a time-series being equal to or greater than the number of years of sampling required to detect a 5% annual change in Hg concentrations, with a significance level of P<0.05 and 80% statistical power. At the time of the previous AMAP Assessment, only 10% of the Hg time-series were deemed adequate. If an additional 5years of data were to be added to the current set of time-series, it is predicted that 53% of time-series would become adequate.
authors.affiliation: National Environmental Research Institute (NERI), Aarhus University (AAU), Frederiksborgvej 399, P.O. Box 358, 4000 Roskilde, Denmark. ffr@dmu.dk
authors.forename: Frank
authors.initials: F
authors.lastname: Rigét
authors.forename: Birgit
authors.initials: B
authors.lastname: Braune
authors.forename: Anders
authors.initials: A
authors.lastname: Bignert
authors.forename: Simon
authors.initials: S
authors.lastname: Wilson
authors.forename: Jon
authors.initials: J
authors.lastname: Aars
authors.forename: Erik
authors.initials: E
authors.lastname: Born
authors.forename: Maria
authors.initials: M
authors.lastname: Dam
authors.forename: Rune
authors.initials: R
authors.lastname: Dietz
authors.forename: Marlene
authors.initials: M
authors.lastname: Evans
authors.forename: Thomas
authors.initials: T
authors.lastname: Evans
authors.forename: Mary
authors.initials: M
authors.lastname: Gamberg
authors.forename: Nikolaus
authors.initials: N
authors.lastname: Gantner
authors.forename: Norman
authors.initials: N
authors.lastname: Green
authors.forename: Helga
authors.initials: H
authors.lastname: Gunnlaugsdóttir
authors.forename: Kurunthachalam
authors.initials: K
authors.lastname: Kannan
authors.forename: Robert
authors.initials: R
authors.lastname: Letcher
authors.forename: Derek
authors.initials: D
authors.lastname: Muir
authors.forename: Pat
authors.initials: P
authors.lastname: Roach
authors.forename: Christian
authors.initials: C
authors.lastname: Sonne
authors.forename: Gary
authors.initials: G
authors.lastname: Stern
authors.forename: Oystein
authors.initials: O
authors.lastname: Wiig
mesh_terms: D000818:Animals; D001110:Arctic Regions; D058448:Biota; D004784:Environmental Monitoring; D004785:Environmental Pollutants / Q000378:metabolism*; D004787:Environmental Pollution / Q000706:statistics & numerical data; D008628:Mercury / Q000378:metabolism*
chemical_list: D004785:Environmental Pollutants; D008628:Mercury