Métodos Analíticos

In order to maintain efficient sampling, communities were treated as two-dimensional (Meese and Tomich, 1992; Van Rein et al., 2011). Benthos from the images collected from FaAlCr habitats were identified and recorded to the lowest taxonomic level using up-to-date identification manuals (Cornelius, 1995; Hayward and Ryland, 1995;

Bunker et al., 2012; Wood, 2013b; Wood, 2013a) and expert knowledge. Epibenthos from the images collected from Lhyp.Pk habitats was identified to broad benthic categories of coarse taxonomic resolution to represent their structural role within the community. This was due to difficultly identifying individuals to species level (partially obstructed by shadows from, or overlapping algae) as well as the need for

microscopic equipment for identification of many algal species. The groups selected (Van Rein et al., 2012) were: red foliose algae > 1cm height above the substratum, non-red foliose algae > 1cm height above the substratum, red algal turf <1cm height above the substratum, non-red algal turf < 1cm height above the substratum,

encrusting algae, mixed hydrozoan/bryozoan turf <1cm, feather hydroids and encrusting bryozoans.

Benthos which could not be identified because it was too far away (e.g. in a crevice) or too dark was excluded from the analysis. Mobile species were also excluded from the analysis. Percentage cover was recorded using a point method (Aronson et al., 1994), which is time efficient, unbiased by observers, and is more sensitive to

changes in community composition than visual estimation and frequency occurrence methods (Aronson et al., 1994; Drummond and Connell, 2005; Van Rein et al., 2012). Images were overlaid with 100 evenly placed points (Drummond and Connell, 2005), using photoQuad (v1.0) (Trygonis and Sini, 2012), the epibenthos under each

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point was identified and represented 1% of the image area (or 6.25cm²) (Fig 6.5, b).

Species abundances from a random sample of 20 images from each habitat were recorded using the point intercept method by a second experienced ecologist and species abundances were cross verified to the original abundances estimates. All cross verified images did not significantly differ in abundance or species composition between researchers.

6.2.5. Data analysis

The location of the pot or anchor weight along the transect was recorded during every dive. The data for the site were divided into 5 sections (north-south) along the transect (every 5m) (Fig 6.4). For each site, images from a 10 x 10m area, which had experienced maximum impact, were used for analysis. Data pre fishing, termed

‘Baseline’ data (B) were compared to the same areas after fishing, termed ‘Impact’

(I). To ensure any changes detected were potting impacts and not natural variation, data for control sites were analysed before and after (referred to as Control Baseline (CB) and Control Impact (CI) data, respectively). The spatial scale used for this

experimental work was purposely small-scale in order to investigate any impacts with a high degree of accuracy. Experimental fishing studies provide useful insights into direct impacts, and relative severity of these as well as investigating habitat – fishing gear interactions (Kaiser et al., 2006; Hinz et al., 2009). However, results from small-scale experimental fishing impact studies are difficult to extrapolate to an ecosystem-wide scale which are ideally required for EBFM (Hiddink et al., 2006; Hinz et al., 2009). However, due to the limited research on potting impacts to date, robust and focused experimental evidence is first required (Eno et al., 2001; Gray et al., 2006).

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Fig 6.4. Diagram of example study site (Impact area (25 x 10m) and a control area (green line and squares, 5 x 10m) showing randomly distributed sample areas (grey squares, 0.25 x 0.25m² each), ≈ 20kg anchor weights (red circles), tape measures used for transects (dotted black line). Impact zones (red line, every 5m) are shown.

A total of 920 images were analysed (40 images for each B, I, CB and CI per site) for both high and low fished FaAlCr areas and 460 images were analysed (40 images for each B, I, CB and CI per site) for high fished Lhyp.Pk areas. Benthic community data were square root transformed to reduce dominance of common taxa (Martin et al., 2012) and Bray Curtis similarity matrices produced. Exploratory statistics including multivariate analysis (cluster dendograms and multi-dimensional scaling plots) were conducted using PRIMER (v.6).

Differences in community data were explored between experimental treatments

‘Baseline’ – ‘Control Baseline’, ‘Baseline – Impact’, ‘Control Baseline’ – ‘Control Impact’ and ‘Impact’ – ‘Control Impact’ using mixed models in PERMANOVA (v.

1.0.5) (type III sum of squares, under a reduced model with 999 permutations) following a 2 factor design with interaction (site as a random factor with 3 levels: site 1, 2, 3, and treatment type as a fixed factor with 2 levels: B – CB, B – I, CB – CI, I -CI) for FaAlCr and Lhyp.Pk habitats. Tests of homogeneity of dispersion (PERMDISP routine in PERMANOVA) were used to test the null hypothesis of no difference in dispersion among a priori groups. Post-hoc analysis using the pairwise function in PERMANOVA (v. 1.0.5) investigated which factor levels were responsible for significant interactions (type III sum of squares, under a reduced model with 999 permutations).

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The components of variation were estimated to provide a measure of the multivariate variability between factors within data sets (Anderson et al., 2008; Martin et al., 2012;

Van Rein et al., 2012). Due to the use of Bray-Curtis similarity matrices, the estimates of components of variation can be directly interpreted as percentage dissimilarity of conditions within experimental factors (Anderson et al., 2008).

Variability associated with factor ‘site’ indicates general spatial variability, that associated with factors B – CB, B – I, CB – CI, I -CI indicates temporal variability and that associated with ‘Residuals’ indicates more specifically the residual variability among replicate photoquad samples (Anderson et al., 2008).

To visualize multivariate patterns, principal coordinate analysis (PCO) was used (PERMANOVA v. 1.0.5) because the variation explained by the axes of all plots was high, these were able to capture the high-dimensional structure adequately and thus provide a closer reflection of the resemblance values used in the partitioning

methods for PERMANOVA than a non-metric MDS plot (Anderson et al., 2008).

There were too many samples to visually examine differences in assemblages

between treatments B, I, CB and CI in a single ordination (n = 480) therefore centroids of each treatment and site were produced and plotted using PCO (Terlizzi et al., 2005).

Similarity of percentage analysis (SIMPER, Clarke (1993)) was used to identify the percentage similarity that benthos contributed to the measure of Bray-Curtis

Similarity for treatments (B – I and CI – CB). This analysis allowed identification of benthos that were most important in differentiating between treatments. Benthos were selected as important if they contributed to > 10% dissimilarity and if the dissimilarity/standard deviation ratio was > 1 (an indicator of consistency in contribution to dissimilarity across samples) (Terlizzi et al., 2005; Clarkre, 1993).

A total of 72 kelp abundance recordings were obtained from Lhyp.Pk habitats.

Differences in kelp abundance between treatments were tested for significance in PERMANOVA v. 1.0.5 (type III sum of squares, under a reduced model with 999 permutations) following a 2 factor design with interaction (site as a random factor with 3 levels: site 1, 2, 3, and treatment type as a fixed factor with 2 levels: B – CB, B – I, CB – CI, I –CI).

156 6.3. Results