Rinde E et al. (2014), The influence of physical factors on kelp and s...

ECB-ART-43468

PLoS One 2014 Jan 01;96:e100222. doi: 10.1371/journal.pone.0100222.

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The influence of physical factors on kelp and sea urchin distribution in previously and still grazed areas in the NE Atlantic.

Rinde E , Christie H , Fagerli CW , Bekkby T , Gundersen H , Norderhaug KM , Hjermann DØ .

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The spatial distribution of kelp (Laminaria hyperborea) and sea urchins (Strongylocentrotus droebachiensis) in the NE Atlantic are highly related to physical factors and to temporal changes in temperature. On a large scale, we identified borders for kelp recovery and sea urchin persistence along the north-south gradient. Sea urchin persistence was also related to the coast-ocean gradient. The southern border corresponds to summer temperatures exceeding about 10°C, a threshold value known to be critical for sea urchin recruitment and development. The outer border along the coast-ocean gradient is related to temperature, wave exposure and salinity. On a finer scale, kelp recovery occurs mainly at ridges in outer, wave exposed, saline and warm areas whereas sea urchins still dominate in inner, shallow and cold areas, particularly in areas with optimal current speed for sea urchin foraging. In contrast to other studies in Europe, we here show a positive influence of climate change to presence of a long-lived climax canopy-forming kelp. The extent of the coast-ocean gradient varies within the study area, and is especially wide in the southern part where the presence of islands and skerries increases the area of the shallow coastal zone. This creates a large area with intermediate physical conditions for the two species and a mosaic of kelp and sea urchin dominated patches. The statistical models (GAM and BRT) show high performance and indicate recovery of kelp in 45-60% of the study area. The study shows the value of combining a traditional (GAM) and a more complex (BRT) modeling approach to gain insight into complex spatial patterns of species or habitats. The results, methods and approaches are of general ecological relevance regardless of ecosystems and species, although they are particularly relevant for understanding and exploring the corresponding changes between algae and grazers in different coastal areas.

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Genes referenced: LOC100887844 LOC100893907 LOC115925415

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	Figure 1. The study area.Map of the study area along the north Norwegian coast 65Â°â€“68Â°N, within the northeast Atlantic, coded for sampling year. Large points (nâ€Š=â€Š1220) are model data for the statistical analyses, whereas small yellow points (nâ€Š=â€Š403) are test data for evaluation of the models.
	Figure 2. GAM for kelp recovery.The partial response plots of the best GAM (lowest AICc) for presence of the kelp Laminaria hyperborea.
	Figure 3. Interactions in the BRT model for kelp recovery.Predictions based on the main interactions from the BRT model of kelp Laminaria hyperborea recovery: wave exposure x depth (left, interaction size â€Š=â€Š48.9) and curvature x depth (right, interaction size â€Š=â€Š40.9).
	Figure 4. GAM for sea urchin persistence.The partial response plots of the best GAM without interactions for presence of sea urchin Strongylocentrotus droebachiensis.
	Figure 5. Interactions in the BRT model for sea urchin persistence.Predictions based on the main interactions from the BRT model of occurrence/persistence of sea urchin Strongylocentrotus droebachiensis: depth x latitude (left, interaction size â€Š=â€Š205.5) and minimum current speed x latitude (right, interaction size â€Š=â€Š91.2).
	Figure 6. The extent of kelp recovery within the study area.Size of the predicted area (in km2 on the left axis and in percent of the study area on the right axis) modeled to be areas with recovery of kelp (Laminaria hyperborea, lh) and with persistence of sea urchins (Strongylocentrotus droebachiensis, sd), given different cut-off probabilities for the best GAMs and for the BRT models. The low and high optimal threshold lines show the lowest and highest cut-off value according to the Youden index for the best models.
	Figure 7. Maps showing predicted kelp recovery and sea urchin persistence.Predicted recovery of kelp Laminaria hyperborea (orange) and persistence of sea urchin Strongylocentrotus droebachiensis (green) based on the BRT models in region 1 (65Â°N, lower panel), region 3 (67Â°N, middle panel) and region 5 (69Â°N, upper panel). The map further shows land (beige), wave exposed areas where kelp never has been grazed (leather brown), field observed presences of L. hyperborera (brown circles), S. droebachiensis (violet circles) and absences of both species (open circles). White areas include shallow areas with predicted absences of the two species, areas deeper than 30 meters, as well as sheltered areas that are excluded from the predicted area. Scale 1âˆ¶100 000.
	Figure 8. Summer sea water temperature along latitude in the period 1990 to 2007.Modeled sea water temperature at 10(left) and July (right) at latitudes between 63â€“70Â°N in the period 1990 to 2007, at an intermediate position along the coast-ocean gradient. The analysis is based on data from the Temperature Atlas developed by [28].

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