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  • 1
    Publication Date: 2017-01-27
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , peerRev , info:eu-repo/semantics/article
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  • 2
    Publication Date: 2017-06-20
    Description: The coral species Paragorgia arborea and Primnoa resedaeformis are abundant and widely distributed gorgonians in North Atlantic waters. Both species add significant habitat complexity to the benthic environment, and support a host of invertebrate species. Mapping their distribution is an essential step in conservation and resource management, but challenging as a result of their remoteness. In this study, three predictive models — Ecological Niche Factor Analysis, Genetic Algorithm for Rule-set Production and Maximum Entropy modeling (MaxEnt) were applied to predict the distribution of species' suitable habitat across a region of Røst Reef (Norwegian margin) based on multiscale terrain variables. All three models were successful in predicting the habitat suitability for both gorgonian species across the study area, and the MaxEnt predictions were shown to outperform other predictions. All three models predicted the most suitable habitats for both species to mainly occur along the ridges and on the upper section of the large slide, suggesting both species preferentially colonize topographic highs. Jackknife tests for MaxEnt predictions highlighted the seabed aspect in relation to P. arborea distribution, and the seabed relative position (curvature) in relation to the distribution of both species. Given the vulnerability of deep-water corals to anthropogenic impacts, further comparative study over a wider study area would be particularly beneficial for the management of the species.
    Type: Article , PeerReviewed
    Format: text
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  • 3
    Publication Date: 2017-06-20
    Description: The spatial variability in the mix of species making up Cold-water coral reef communities is not well known. In this study abundances of a selection of megafauna (Lophelia pertusa, Madrepora oculata, Paragorgia arborea, Primnoa resedaeformis, Mycale lingua, Geodia baretti, Acesta excavata and fish) were quantified throughout 9 manned submersible video transects from 3 reef complexes (Røst Reef, Sotbakken Reef and Traena Reef) on the Norwegian margin. Substrate type (coral structure, rubble, exposed hardground or soft sediment) was also recorded. Variations in the densities of these fauna (with respect to both reef complex and substrate type) were investigated, with spatial covariance between species assessed. For the majority of fauna investigated, densities varied by both reef and substrate. Spatial covariance indicated that some species may be utilising similar habitat niches, but that minor environmental differences may favour colonisation by one or other at a particular reef. Fish densities were generally higher in regions with biogenic substrate (coral structure and coral rubble substrates) than in areas of soft or hardground substrate. Further, fish were more abundant at the northerly Sotbakken Reef at time of study than elsewhere. Community structure varied by reef, and therefore management plans aimed at maintaining the biodiversity of reef ecosystems on the Norwegian margin should take this lack of homogeneity into account.
    Type: Article , PeerReviewed , info:eu-repo/semantics/article
    Format: text
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  • 4
    Publication Date: 2017-06-20
    Description: Investigating the relationship between deep-water coral distribution and seabed topography is important for understanding the terrain habitat selection of these species and for the development of predictive habitat models. In this study, the distribution of the deep-water gorgonians, Paragorgia arborea and Primnoa resedaeformis, in relation to terrain variables at multiple scales of 30 m, 90 m and 170 m were investigated at Røst Reef, Traena Reef and Sotbakken Reef on the Norwegian margin, with Ecological Niche Factor Analysis applied. To date, there have been few published studies investigating this aspect of gorgonian distribution. A similar correlation between the distribution of P. arborea and P. resedaeformis and each particular terrain variable was found at each study site, but the strength of the correlation between each variable and distribution differed by reef. The terrain variables of bathymetric position index (BPI) and curvature at analysis scales of 90 m or 170 m were most strongly linked to the distribution of both species at the three geographically distinct study sites. Both gorgonian species tended to inhabit local topographic highs across all three sites, particularly at Sotbakken Reef and Traena Reef, with both species observed almost exclusively on such topographic highs. The tendency for observed P. arborea to inhabit ridge crests at Røst Reef was much greater than was indicated for P. resedaeformis. This investigation identifies the terrain variables which most closely correlate with distribution of these two gorgonian species, and analyzes their terrain habitat selection; further development of predictive habitat models may be considered essential for effective management of these species.
    Type: Article , PeerReviewed
    Format: text
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  • 5
    Publication Date: 2019-02-01
    Description: An understanding of how terrain features influence abundance of a particular species greatly aids in the development of accurate predictive habitat suitability models. In this study, we investigated the observed seafloor coverage of cold-water coral Lophelia pertusa in relation to seabed topography at the Sotbakken and Røst Reefs on the Norwegian margin. The primary terrain features at the study sites are a SW-NE stretching mound at Sotbakken Reef and SW-NE running ridges at Røst Reef, located at depths of ~300–400 m and ~250–320 m respectively. Ship-borne multibeam bathymetry data, JAGO dive video data and JAGO positioning data were used in this study. Terrain variables were calculated at scales of 30 m, 90 m and 170 m based on the bathymetry data. Additionally, we investigated the relationships between the terrain variables at multiple scales using the Unweighted Pair Group Method.
    Type: Article , PeerReviewed
    Format: text
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