Fig 1.
Location of sampling within the five sampling areas along the designated AC125 (crosshatched polygon), depth and BRUVS deployments (points).
Within the designated AC125, on AC125 is defined as between 115-135m depth based on existing bathymetry for this region [42]. This map was generated using ESRI software.
Table 1.
Spatial and habitat predictors and responses used in analyses of marine communities along the designated AC125.
Table 2.
Data sets derived from multibeam bathymetry that were used as environmental surrogate variables for spatial predictive modelling of fish richness.
Fig 2.
The five areas explored using BRUVS revealed a variety of fish communities.
(a, b) Area 2, (c) Area 3, (d) Area 4, (e, f) Area 5.
Fig 3.
Mean (± SE) fish species richness (a) and relative abundance (MaxN, b) per BRUVS deployment observed on and off the AC125 in five sampling areas.
Table 3.
Depth (m) of deployments in each sampling area along the designated AC125.
Fig 4.
GAMM predicted trends in log transformed fish species richness (a, b) and relative abundance (c) with depth, proportion of rubble and cover of benthic biota (benthos) from best models. Lines indicate the fit of the best-approximating model with 95% confidence intervals (dotted lines). (a) is coloured by AC125 position, highlighting the relationship with depth (pink: Off AC125 shallow; blue: On AC125; purple: Off AC125 deep).
Table 4.
Top three GAMM model fits and fits exclusively with AC125 and area, examining the effects of predictors on log transformed richness (R) and relative abundance (A, summed MaxN).
Fig 5.
Heatmap indicating the key predictors from the GAMM models of most common species relative abundance.
Colours indicate relative importance.
Fig 6.
GAMM predicted trends in relative abundance (MaxN) per BRUVS deployment of common species: Pristipomoides multidens (a); Carangoides caeruleopinnatus (b, c), Epinephelus areolatus (d), Nemipterus bathybius (e, f) and Lagocephalus lunaris (g, h, i), by Area, depth and proportion of rubble and gravel cover from best models. Lines indicate the fit of the best-approximating model with 95% confidence intervals (dotted lines). a-f are coloured by Area 1–5.
Table 5.
Top GAMM models examining the effects of predictors on most common species relative abundance at the five areas.
Fig 7.
Redundancy analysis (dbRDA via capscale) biplot for fish species occurring in at least 10 BRUVS across the five areas, by AC125 position, depth, cover of benthic biota (benthos) and substrate (mud/silt, gravel, rubble, boulder/reef), and visibility.
AC125 position is indicated by squares, circles and diamonds, coloured by Area (a), with the same analysis coloured by AC125 position (b). Weighted averages of site scores (points) are scaled by site richness and significant species vectors (p < 0.001) correlated with linear constraints are indicated.
Table 6.
Permutation tests of the effects of spatial and environmental predictors on the dissimilarity matrix of fish species.
Fig 8.
Boxplot of recorded species depth distributions ordered by mean depth.
Middle lines indicate median depths, whiskers represent ~95% confidence intervals for comparing medians, and points are outliers. Depth range of the AC125 designation is indicated by blue shading.
Fig 9.
Predicted fish species richness across five AC125 study areas.
Bubble size reflects the observed number of fish species in BRUVS deployments.
Fig 10.
Predicted regional fish species richness for the North West Shelf (a) with locations of BRUVS deployments used in model fitting procedures plotted (b, bubble colours indicate the observed species richness) and associated uncertainty surface of the fitted RF model (c). This map was generated using ESRI software.