Dataset Information

Divergent trends in anadromous salmonid populations in Norwegian and Scottish rivers.

ABSTRACT: The Atlantic salmon (Salmo salar) is a charismatic anadromous fish of high conservation and economic value. Concerns have been expressed regarding the long-term viability of fisheries throughout the species's distributional range because of abundance variations that cannot currently be explained or predicted. Here, we analyse long-term catch data obtained over a wide geographical range and across a range of spatial subscales to understand more fully the factors that drive population abundance. We use rod catch data from 84 Norwegian rivers over 125 years (1876-2000) and 48 Scottish rivers over 51 years (1952-2002). The temporal correlation in catches is very long-term, with trends persisting over several decades. The spatial correlation is relatively short-range, indicating strong local-scale effects on catch. Furthermore, Scottish salmon populations exhibit recent negative trends in contrast to some more positive trends in Norway--especially in the north.

SUBMITTER: Vollestad LA

PROVIDER: S-EPMC2679082 | biostudies-other | 2009 Mar

REPOSITORIES: biostudies-other

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Divergent trends in anadromous salmonid populations in Norwegian and Scottish rivers.

Vøllestad Leif Asbjørn LA Hirst David D L'Abée-Lund Jan Henning JH Armstrong John D JD MacLean Julian C JC Youngson Alan F AF Stenseth Nils Chr NC

Proceedings. Biological sciences 20090301 1659

The Atlantic salmon (Salmo salar) is a charismatic anadromous fish of high conservation and economic value. Concerns have been expressed regarding the long-term viability of fisheries throughout the species's distributional range because of abundance variations that cannot currently be explained or predicted. Here, we analyse long-term catch data obtained over a wide geographical range and across a range of spatial subscales to understand more fully the factors that drive population abundance. W ...[more]

PMID: 19129101

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Project description:Eye and hair colour is highly variable in the European population, and is largely genetically determined. Both linkage and association studies have previously been used to identify candidate genes underlying this variation. Many of the genes found were previously known as underlying mutant mouse phenotypes or human genetic disease, but others, previously unsuspected as pigmentation genes, have also been discovered.We assayed the hair of a population of individuals of Scottish origin using tristimulus colorimetry, in order to produce a quantitative measure of hair colour. Cluster analysis of this data defined two groups, with overlapping borders, which corresponded to visually assessed dark versus red/light hair colour. The Danish population was assigned into categorical hair colour groups. Both cohorts were also assessed for eye colour. DNA from the Scottish group was genotyped at SNPs in 33 candidate genes, using 384 SNPs identified by HapMap as representatives of each gene. Associations found between SNPs and colorimetric hair data and eye colour categories were replicated in a cohort of the Danish population. The Danish population was also genotyped with SNPs in 4 previously described pigmentation genes. We found replicable associations of hair colour with the KITLG and OCA2 genes. MC1R variation correlated, as expected, with the red dimension of colorimetric hair colour in Scots. The Danish analysis excluded those with red hair, and no associations were found with MC1R in this group, emphasising that MC1R regulates the colour rather than the intensity of pigmentation. A previously unreported association with the HPS3 gene was seen in the Scottish population. However, although this replicated in the smaller cohort of the Danish population, no association was seen when the whole study population was analysed.We have found novel associations with SNPs in known pigmentation genes and colorimetrically assessed hair colour in a Scottish and a Danish population.

Project description:This dataset [1] quantifies the extent and rate of annual change in surface water area (SWA) across India's rivers and basins over a period of 30 years spanning 1991 to 2020. This data has been derived from the Global Surface Water Explorer, which maps historical terrestrial surface water occurrence globally using the Landsat satellite image archive since 1984, at a spatial resolution of 30 m/pixel and a temporal resolution of once a month. This monthly time-series was used to create annual composites of wet-season (October, November, December), dry-season (February, March, April), and permanent (October, November, December, February, March, April) surface water extent, which were then used to estimate annual rates of change. To estimate SWA trends for both river networks and their basins, we conducted our analysis at two spatial scales - (1) cross-sectional reaches (transects) across river networks, and (2) sub-basins within river catchments. For each reach and sub-basin (henceforth basin), temporal trends in wet-season, dry-season, and permanent SWA were estimated using the non-parametric Sen's slope estimator. For every valid reach and basin, the temporal timeseries of invalid or missing data was also computed as a fractional area to inform the level of certainty associated with reported SWA trends estimates. In addition to a Zenodo data repository, this data [1] is presented as an interactive web application (https://sites.google.com/view/surface-water-trends-india/; henceforth Website) to allow users to visualize the trends of permanent, wet-season, and dry-season water along with the extent of missing data for individual transects or basins across India. The Website provides a simple user interface to enable users to download seasonal time-series of SWA for any region of interest at the scale of the river network or basin. The Website also provides details about accessing the annual permanent, dry and wet season composites, which are stored as publicly accessible cloud assets on the Google Earth Engine platform. The spatial (basin and reach) and temporal (wet season, dry season, and permanent water scenarios) scales of information provided in this dataset yield a granular understanding of water systems in India. We envision this dataset to serve as a baseline information layer that can be used in combination with other data sources to support regional analysis of hydrologic trends, watershed-based analysis, and conservation planning. Specific applications include, but are not limited to, monitoring and identifying at-risk wetlands, visualizing and measuring changes to surface water extent before and after water infrastructure projects (such as dams and water abstraction projects), mapping drought prone regions, and mapping natural and anthropogenic changes to SWA along river networks. Intended users include, but are not limited to, students, academics, decision-makers, planners, policymakers, activists, and others interested in water-related issues.

Dataset Information

Divergent trends in anadromous salmonid populations in Norwegian and Scottish rivers.

Publications

Divergent trends in anadromous salmonid populations in Norwegian and Scottish rivers.

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