Fig 1.
(a) Laminaria hyperborea is a dominant kelp species in the northeast Atlantic, where it forms dense, extensive macroalgal canopies. (b) It forms a large, complex holdfast structure, which anchors the plant to rocky substrata and provides biogenic habitat for associated organisms. (c) Holdfasts sampled were typically encrusted by a high coverage of sessile invertebrates. (d) The interstitial space between the reef surface and the holdfast was utilised by a high diversity of mobile invertebrates. (e) Dense stands of L. hyperborea may serve as ecologically-significant repositories of biodiversity.
Fig 2.
Map indicating the locations of the four study regions in the UK, northeast Atlantic: (A) northern Scotland, (B) western Scotland, (C) southwest Wales and (D) southwest England. Smaller panels show the positions of the 3 study sites within each region.
Table 1.
Predictor variables recorded at 12 study sites within 4 distinct regions in the UK.
‘Mean SST’ is the annual mean temperature calculated from satellite-derived sea surface temperature (SST) data (2005–2014). ‘Log wave fetch’ is a broad-scale metric of wave exposure, derived by summing fetch values calculated for 32 angular sectors surrounding each site (see [71]). ‘Log chl a mean’ is the average annual concentration of chlorophyll a (log10 mg m-3 from MODIS Aqua satellite data, 2002–2012). ‘Peak summer max (mean) temp’ is the maximum (average) daily temperature recorded between 26 July and 18 August 2014, when all sensor array deployments overlapped. ‘Summer daylight’ is the average daytime (08:00–20:00) light intensity during a 14 d deployment of light loggers. ‘Tidal water motion’ is a proxy for water movement driven by tidal flow, derived from the range in water motion values recorded during a 24 h period, averaged over the 45 d accelerometer deployment. ‘Wave water motion’ is a proxy for water movement driven by waves, derived from averaging the 3 highest-magnitude water motion values observed during the 45 d accelerometer deployment (following correction for tidal-movement). ‘PO43-‘ and ‘NO3- +NO2-‘ indicate averaged spring and summer concentrations of phosphate and nitrite + nitrate respectively (n = 4 water samples taken from ~1 m above the kelp canopy).
Fig 3.
Biogenic habitat structure provided by Laminaria hyperborea holdfasts: (a) kelp age, (b) total holdfast volume (THV), (c) habitable holdfast space (HHS) and (d) relative holdfast space (THV/HHS). Values are means of 6 replicate holdfasts per site (± SE).
Table 2.
Results of univariate PERMANOVA to test for differences in habitat metrics (a. kelp age, b. total holdfast volume, c. habitable holdfast space and d. relative holdfast space).
Permutations were based on a Euclidean distance similarity matrix generated from untransformed data. All tests used a maximum of 4999 permutations under a reduced model; significant effects (P<0.05) are shown in bold. An underlined P-value indicates that PERMDISP detected significant differences in within-group dispersion between levels of that factor (P<0.05).
Fig 4.
Univariate assemblage-level metrics for sessile holdfast assemblages: (a) the proportion of major taxonomic groups, (b) sessile assemblage taxon richness, (c) total biomass of sessile organisms, (d) taxa equatorward range edge. Values for (b) and (c) are means of 6 replicate holdfasts per site (±SE).
Fig 5.
mMDS plots depicting the structure of sessile faunal assemblages, with centroids representing (a) individual holdfast samples (b) and site averages. Similarly mMDS plots depicting the structure of mobile faunal assemblages, with centroids representing (c) individual holdfasts and (d) site averages). Labels indicate sites and symbols indicate regions.
Table 3.
Results of multivariate PERMANOVA to test for differences in holdfast sessile (a) and mobile (b) assemblage between regions (fixed) and sites (random, nested within region).
Habitable holdfast space (HHS) was included as a covariable in the analysis. Permutations were based on a Bray-Curtis similarity matrix generated from fourth-root transformed biomass/abundance data. Results of univariate PERMANOVA to test for differences in assemblage-level univariate metrics (taxon richness and total biomass) in holdfast assemblages are also shown (c–f). Permutations for univariate analysis were based on a Euclidean distance similarity matrix generated from untransformed diversity data. All tests used a maximum of 4999 permutations under a reduced model; significant effects (P<0.05) are shown in bold. An underlined P-value indicates that PERMDISP detected significant differences in within-group dispersion between levels of that factor (P<0.05).
Table 4.
DISTLM marginal test results for each environmental predictor variable selected for the most parsimonious model for sessile assemblages.
The best solution based on stepwise selection and AICc criteria is shown. SS = sum of squares (trace), Prop. = proportion of variation explained.
Fig 6.
Univariate assemblage-level metrics for mobile holdfast assemblages: (a) the proportion of major taxonomic groups, (b) mobile assemblage taxon richness, (c) total biomass of mobile organisms, (d) taxa equatorward range edge. Values for (b) and (c) are means of 6 replicate holdfasts per site (±SE).
Table 5.
DISTLM marginal test results for each environmental predictor variable selected for the most parsimonious model for mobile assemblages.
The best solution based on stepwise selection and AICc criteria is shown. SS = sum of squares (trace), Prop. = proportion of variation explained.